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\Volume{29}
\Number{2}
\Month{June}
\Year{2022}
%%%%\Issuetitle{Special Issue: XXXXXXXX}

\received{2021}{10}{1}
\revised{2021}{12}{13}
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\accepted{2022}{1}{26}

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\esubject{General Paper}
\etitle{Extracting Citizen Feedback from Social Media \\ by Appraisal Opinion Type Viewpoint}
\eauthor{Tetsuya Ishida\affiref{Affiliation_1a} \and Yohei Seki\affiref{Affiliation_1b} \and Wakako Kashino\affiref{Affiliation_2} \and Noriko Kando\affiref{Affiliation_3}\affiref{Affiliation_4}}
\eabstract{
Citizen feedback is essential for improving hospitality in government policies and customer services. In this study, we propose a method for extracting citizen feedback from social media according to appraisal opinion type by filtering tweets based on multiple viewpoints such as regional dependency, status of citizen, and polarity. To improve the F1-score of the estimation of opinion unit viewpoints, we implement a multitask learning framework to estimate associated viewpoints using a BERT model. In the experiment, we focus on two domains of citizen life during the COVID-19 pandemic: nursery school life and restaurant takeout services. Our multitask learning approach was effective in estimating viewpoints on opinions. In addition, we demonstrate that citizen feedback filtering based on specific viewpoints is valuable in investigating chronological opinion transitions by appraisal opinion types.
}
\ekeywords{Sentiment Analysis, Citizen Sentiment, Appraisal Theory, Multitask Learning, COVID-19}

\headauthor{Ishida et al. }
\headtitle{Extracting Citizen Feedback from Social Media by Appraisal Opinion Type Viewpoint}

\affilabel{Affiliation_1a}{}{Graduate School of Comprehensive Human Sciences, University of Tsukuba}
\affilabel{Affiliation_1b}{}{Faculty of Library, Information and Media Science, University of Tsukuba}
\affilabel{Affiliation_2}{}{National Institute for Japanese Language and Linguistics}
\affilabel{Affiliation_3}{}{National Institute of Informatics}
\affilabel{Affiliation_4}{}{The Graduate University for Advanced Studies (SOKENDAI)}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}

\maketitle


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}

During the COVID-19 pandemic, extracting citizens' feedback to improve local government and nongovernmental customer service-related efforts and prevent the spread of infectious diseases is essential for citizens to enjoy a social life. To improve policy guidance, local governments have established systems for citizen participation in the government, such as public comment systems and citywide competition for council memberships. However, these existing systems do not attract interest from a sufficient number of residents and are biased toward attitudinal participants. Therefore, a new method is required to obtain a large amount of unbiased and experienced citizen feedback.

In this paper, we propose a new method for extracting citizens' opinions from Twitter.\footnote{\url{https://twitter.com}} On Twitter, many users casually express their daily feelings; therefore, a variety of citizens' opinions can be easily extracted. Many conventional opinion analysis studies have focused on polarity, such as positive or negative \cite{dimitrov2020cikm}, and emotion classification, such as joy and sadness \cite{Lwin2020}. However, because of the wide variety of citizens' opinions on Twitter, specific viewpoints, such as polarity and types of emotions, are insufficient for analyzing citizens' opinions. Therefore, we categorize various citizens' opinions on Twitter by referring to appraisal theory \cite{appraisal} to extract and organize them by analyzing opinions and tweets from multiple viewpoints. In this study, we analyzed opinions and tweets from multiple viewpoints using a BERT model \cite{bert}. We then extract citizen feedback according to specified conditions from multiple viewpoints.

In our citizen-opinion extraction, we suppose each viewpoint of an opinion is classified simultaneously. In this manner, we can expect the effectiveness of multitask learning \cite{multitask}, in which multiple tasks are performed simultaneously using a common model, and a higher F1-score can be achieved than when each task is performed independently. Therefore, we investigated the improvement in the F1-score of opinion extraction by applying multitask learning to the estimation tasks of multiple viewpoints.

In addition, the opinions of citizens change depending on the social impact of the policies of their local governments \cite{Lwin2020,kuan2020,naskar2020,Pagolu2016}. Therefore, we extracted citizens' opinions by specifying the posting time according to the social impact of the local government's policies related to COVID-19. In addition, because the infection status of COVID-19 varies from city to city \cite{Fu2020}, we analyzed the tweets focusing on citizens in one city.

In the experiment, we investigated the effect of the multitask learning approach and analyzed the rise and fall of the frequency of citizens' opinions specified by viewpoints to associate their relationship with the influence of social impact. We focused on Yokohama City, where the largest number of citizens live in Japan, and extracted citizens' opinions in the following two domains during the COVID-19 outbreak:
\begin{enumerate}
    \item ``Nursery schools'' that became a major problem because of school closures and the voluntary restraint on school attendance to protect infants from the virus.
    \item ``Takeout services'' that became essential services to avoid contact with the infection.
\end{enumerate}

The contributions of our work are summarized as follows:
\begin{enumerate}
    \item To the best of our knowledge, this is the first study regarding citizens' opinions on the COVID-19 pandemic by referring to appraisal theory for extracting citizens' feedback and focusing on the opinion holder's emotional reaction to the targets of their opinions, such as ``behavior or actions of a person or organization'' and ``things or events.''
    \item We defined a framework for extracting valuable citizen feedback from tweets flexibly by combining multiple viewpoints, such as ``polarity,'' ``communication opinion type,'' ``domain relevance,'' ``status of citizen,'' and ``regional dependency'' in addition to the ``appraisal opinion type.''
    \item We proposed a multitask learning framework that uses BERT to estimate the viewpoints of citizens' opinions by focusing on opinion units.
\end{enumerate}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Related Work}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Appraisal Theory}\label{2-1}

Appraisal theory is a system that organizes the interpersonal metafunction of systemic functional linguistics \cite{SFL} in terms of discourse semantics. \citeA{appraisal} proposed that the meaning of interpersonal metafunctions appearing in a text consists of three systems: ``appraisal,'' ``negotiation,'' and ``involvement,''\footnote{While ``negotiation'' focuses primarily on the interactive aspects of discourse, such as the determination of turns of speech, and ``involvement'' refers to elements such as prosody and interjections that deepen relations and use of slang and technical/specialized lexis to signal group affiliation, ``appraisal'' focuses on how writers/speakers establish or emphasize evaluation. In this study, as in appraisal theory, we focused only on ``appraisal.''} based on the belief that the meaning of interpersonal metafunctions is an emotion or dialogue with a putative reader. ``Appraisal'' is composed of three systems: ``attitude,'' ``engagement,'' and ``graduation.'' ``Attitude'' refers to feelings, specifically emotional reactions, judgments of behavior, and evaluations of things. It can be classified into three categories: ``affect,'' ``judgment,'' and ``appreciation.'' According to appraisal theory, these categories of attitude include both positive and negative opinions. ``Engagement'' is an element that distinguishes expressions about opinions in discourse that vary according to the position of the opinion expresser.\footnote{It can be categorized as ``disclaim,'' which indicates taking a position opposite to another position; ``proclaim,'' which indicates taking a positive position toward a certain position; ``entertain,'' which indicates taking a position proposing the subjectivity of the opinion expresser; and ``attribute,'' which indicates taking a position proposing a new perspective.} ``Graduation'' is an element that indicates the strength of each opinion in terms of ``attitude'' and strength of the position of the opinion expresser in terms of ``engagement.'' Graduation is composed of two components: ``force'' (intensity or quantity) and ``focus'' (clarity of the concept).

 \citeA{seki2014} analyzed the differences in opinions that appeared in different categories of questions on Yahoo! Chiebukuro using the system of ``attitude'' in appraisal theory and demonstrated that judgments of behavior tended to appear more frequently in the questions and answers of political categories. \citeA{mora2018} analyzed the review data of online stores using the ``attitude'' system and showed that evaluations of things tended to appear more frequently in the review data. Thus, the target of the most common opinions, such as the behavior of a person or a thing, differs depending on the category. In addition, citizen sentiment, which focuses only on the opinion holder's emotional reaction, is important as feedback for government policy. Therefore, we use the system of ``attitude'' as a viewpoint to distinguish and filter opinions by the differences in the target and the opinion holder's emotional reaction.

The differences in the use of appraisal theory in this study and related studies can be explained as follows. \citeA{seki2014} and \citeA{mora2018} used appraisal theory to manually analyze document categories that reflect the intentions of contributors, such as question categories on Yahoo! Chiebukuro and review data from the Google Play Store, and reveal the opinions that are characteristic of each intention. In addition, they manually analyzed each category and further divided each opinion in the ``attitude'' of appraisal theory into subtypes; for example, ``veracity'' and ``capacity'' for ``judgment.'' In this study, we extract valuable citizen feedback from tweets, the categories of which are not predetermined, by focusing on the opinion holder's emotional reaction or opinion target using a machine learning method. Therefore, we use three categories of attitudes in appraisal theory to distinguish and filter citizens' affect (e.g., mothers' dissatisfaction with their children's lives or satisfaction with a restaurant), judgment of behavior (e.g., judgment of the behavior of nursery or restaurant staff), and appreciation of things (e.g., evaluation of the nursery's facilities or the restaurant's takeout products).


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\subsection{Opinion Typology for Analyzing Citizen Feedback}
\label{2-2}

For opinion analysis on Twitter, many researchers have conducted classification tasks of emotion typology for tweets \cite{Dini2016,Jabreel2019,semeval2018,purver2012eacl}.
 \citeA{Dini2016} indicated that a conventional opinion analysis study assumes that all tweets contain opinions; therefore, in addition to the sentiment classification task for tweets, they performed a classification task for tweets that do and do not contain opinions. \citeA{Jabreel2019} designed a text classification task for a single emotion and proposed a method for detecting multiple emotions expressed in tweets using attention \cite{Attention}. \citeA{Otsuka2007} analyzed a questionnaire on road planning by assigning annotation tags such as in the ``suggestions,'' ``requests,'' and ``recognition of the current situation'' to the corpus, referring to {\it linguistic modality} and {\it speech act theory}. In this study, we extracted multiple opinions from a tweet by segmenting the tweet into sentences and the opinion units introduced in Section \ref{sec3}.

Many sentiment analyses for fighting COVID-19 have recently been published \cite{alamoodi2021}. \citeA{Lwin2020} analyzed the frequency of emotions (e.g., fear, anger, sadness, and joy) in COVID-19-related tweets in chronological order using an original algorithm called CrystalFeel \cite{Gupta2018} and demonstrated that the dominant emotion was different for each chronological series. 
 \citeA{Toriumi2020} investigated trends in Twitter users' emotions during the COVID-19 pandemic and demonstrated that users' emotions changed drastically when a famous Japanese comedian died. 
In addition, \citeA{kuan2020} analyzed the life cycle of rumors on Twitter and concluded that as rumors spread, informative tweets increased and emotional tweets decreased. \citeA{naskar2020} used tweets on multiple topics to analyze the factors that influence Twitter users' emotional changes. \citeA{Pagolu2016} analyzed the chronological association between the polarity of opinions expressed in tweets and stock prices.

These studies have shown that user sentiment on Twitter changes depending on the social situation and environment in which the user is situated. However, these studies cannot reflect opinions and attitudes toward society because they analyze from a specific perspective. Therefore, by analyzing citizens' opinions from multiple viewpoints, including appraisal opinion types and analyzing the frequency of the appearance of citizens' opinions chronologically, we aim to reflect citizens' opinions and attitudes toward society at specified time ranges.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{BERT and Multitask Learning}
\label{2-3}

We used BERT \cite{bert} to analyze tweets. BERT can be pretrained on a large set of data and fine-tuned; our task requires a relatively smaller set of data than typically used. In addition, BERT has shown high performance in many natural language processing tasks, including sentiment analysis. Therefore, we analyzed tweets from multiple perspectives using a fine-tuned BERT.

Multitask learning \cite{multitask} uses a common model to learn multiple related tasks simultaneously, thereby achieving a higher accuracy than learning each task independently. In machine learning, an inductive bias that assumes unknown data is important for the model to predict unknown data that do not appear in the training data \cite{Mitchell80}. In multitask learning, information obtained by learning different tasks can act as an inductive bias, which can improve the generality of the model and enhance the prediction performance for unknown data.

 \citeA{mtdnn} showed that a multitask deep neural network (MT-DNN), which is a BERT-based multitask learning method, performed well on many tasks in natural language understanding, including sentiment analysis. In addition, \citeA{yadev2020} improved the accuracy of the depression detection task by sharing the parameters that weigh each layer BERT between tasks and between layers.

To analyze tweets from multiple viewpoints, we constructed a multitask learning model using BERT, referring to MT-DNN, which has high performance in many natural language understanding tasks.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Viewpoint Estimation with Multitask Learning on Opinion Units}
\label{sec3}

In this section, we propose a method for extracting citizens' opinions from Twitter using multiple viewpoints.

In the proposed method, a corpus for citizen opinion analysis is manually created by assigning multiple viewpoints to tweets. Section~\ref{sec4-1} describes the corpus. We then train an estimation model for each viewpoint, using the created corpus for citizens' opinion analysis as training data. Finally, by inputting a large number of unknown tweets into the trained model and estimating the viewpoints, multiple viewpoints are automatically assigned to all tweets. By specifying an arbitrary number of conditions from these viewpoints, extracting only the opinions of citizens that satisfy all specified conditions from a large number of unknown tweets is possible. In addition to these viewpoints, the conditions of the tweets to be extracted are specified by the posting date.

In Section \ref{sec3-1}, we introduce the viewpoints defined in the proposed method, and in Section \ref{sec3-2}, we explain the estimation model for each viewpoint.


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\subsection{Viewpoints for Citizen Feedback from Tweets}
\label{sec3-1}

In this study, we targeted two domains: nursery schools and (restaurant) takeout services. We introduce viewpoints to be assigned across these domains and domain-specific viewpoints.

We assume that an entire tweet is too large for a target to assign viewpoints directly related to opinions, because multiple-opinion expressions often appear in a single tweet. Therefore, viewpoints directly related to opinions are assigned to sentences and clauses that contain only one opinion as a unit (opinion units), and the tweet is segmented into smaller pieces, as in the opinion clause \cite{seki2010ntcirov}.

We also defined viewpoints for the entire tweet across the two domains, focusing on the regional dependency of the city, domain relevance, and status of the citizen (author).

As described in Section \ref{2-2}, \citeA{Otsuka2007} categorized opinions to analyze citizens' feedback based on {\it linguistic modality} and {\it speech act theory}. These categories are not addressed in appraisal theory. Because we expect these to improve the coverage of citizen opinions, we defined the ``communication opinion type'' viewpoint. In addition, as in Section \ref{2-1}, ``affect,'' ``judgment,'' and ``appreciation,'' which belong to the ``attitude'' category, include both positive and negative opinions and are not distinguished. Therefore, we defined the ``polarity'' viewpoint. To extract tweets that were relevant to the domain rather than those matching the query keywords, we defined the ``domain relevance'' viewpoint. The domain relevance viewpoint filters tweets that contained keywords but were irrelevant to the domain, such as ``I ate dinner at a restaurant near the nursery school'' in the nursery school domain or ``I took my work home'' in the takeout service domain, to avoid extracting such tweets as citizen opinions. In addition, we defined the ``regional dependency'' viewpoint because we required tweets relevant to the region. If tweets were irrelevant to the region, such as in ``Although Japan is an advanced country, everything is inadequate: nursery schools, nursery staff, masks, toilet paper, etc.,'' the comments will be noisy. Consequently, we filtered these tweets using the ``regional dependency'' viewpoint to improve the services of local governments.

The domain-specific viewpoint is relevant to a specific topic and assigned to the entire tweet. By training a model to determine the relevance of a particular topic in advance, opinions related to a particular topic can be extracted. 
We defined ``domain-specific'' viewpoints manually for each domain to achieve an accurate opinion extraction, instead of using a simple query matching approach. We may avoid this manual definition cost and generalize the proposed method when extending it to new domains by filtering tweets relevant to specific subtopics using an unsupervised approach, such as topic models. However, manually determining whether the subtopics are appropriate is still necessary for extracting citizens' feedback. Moreover, the accuracy of the estimation method decreases owing to the lack of a supervisory signal. Therefore, we manually defined ``domain-specific'' viewpoints to prioritize accuracy rather than generality.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Viewpoints for Opinion Units}

\begin{itemize}

\item Appraisal Opinion Type\\
To extract citizen feedback from various types of opinions, we differentiate them according to the opinion target: when we improve customer service, the opinion about the behavior of the staff is valuable; when a product review matters, we need the evaluative opinion of the product. Therefore, based on appraisal theory, we define the appraisal opinion type that focuses on the opinion target. The values of this viewpoint are expressions of feelings (corresponding to ``affect'' under ``attitude'' in appraisal theory), judgment opinions for the behavior or actions of a person or organization (corresponding to ``judgment'' under ``attitude'' in appraisal theory), evaluative opinions directed at a thing or event (corresponding to ``appreciation'' under ``attitude'' in appraisal theory), and no opinion.
\item Polarity\\
Citizens' negative opinions are valuable when searching for improvement points in hospitality, administrative, and customer services; we also need positive word-of-mouth opinions to evaluate a strong model for infectious diseases or a good approach by the staff. In this manner, we define the polarity of a situation in which citizens' opinions are used. The values for this viewpoint are positive, negative, neutral, and no opinion. Typical ``no opinion'' values include facts such as ``I went nursery school to pick my child up'' and informative comments such as ``Today, we will be selling takeout lunches.'' Moreover, a typical neutral value includes opinion units with communication opinion types.
\item Communication Opinion Type\\
We define communication opinion types relevant to the intentions or actions of citizens. The values of this viewpoint are speculation, suggestion, question, request, and no opinion. The definitions and examples of each value are as follows: ``speculation:'' estimate the state of things or the future based on current knowledge, for example, ``the nursery school might be closed;'' ``suggestion:'' propose an idea, for example, ``the nursery school should be closed;'' ``question:'' ask information, for example, ``Will the nursery school be closed?;'' and ``request:'' demand something based on personal needs, for example, ``I want the nursery school to be closed.''
\end{itemize}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Viewpoints for Tweets}

\begin{itemize}

\item Regional Dependency\\
Whether an opinion is specific to citizens living in a target city is an important factor in the extraction of citizen feedback. Region-dependent tweets are defined as those referring to the name of a place, such as a city or prefecture, or to the name of a facility, such as a restaurant in a region. The values of this viewpoint are either dependent or independent.
\item Domain Relevance\\
This viewpoint is defined to avoid extracting opinions from tweets irrelevant to the domain, but include relevant terms. This viewpoint is either relevant or irrelevant.
\item Status of citizen (author)\\
Even if the targets of opinions concern the same entity, the direction of the opinions differs depending on the status of the citizen. Therefore, we require a viewpoint to determine the type of citizen posting the tweet. The values of this viewpoint are parents with small children, nursery school staff, and others in the nursery school domain; and people who used the stores, restaurant staff, and others in the takeout service domain.
\end{itemize}

\textbf{Viewpoints in the Nursery School Domain}
\begin{itemize}

\item Relevance to School Closures and Restraint of School Attendance\\
This viewpoint determines the relevance to nursery school closures and school attendance restraint, which became major problems because of COVID-19. This viewpoint is either relevant or irrelevant.
\item Relevance to Nursery School Capacity\\
This viewpoint determine the relevance to children on waiting lists for childcare services, or a nursery school's capacity, such as acceptance or rejection from nursery schools, which became major issues. This viewpoint is either relevant or irrelevant.
\end{itemize}

\textbf{Viewpoints in the Takeout Service Domain}

\begin{itemize}

\item Relevance to Product Aspect\\
This viewpoint determines whether the evaluation includes the taste and quantity of the product, and the condition in which it is served. This viewpoint is either relevant or irrelevant.
\end{itemize}

\subsection{Estimation Method for Each Viewpoint}\label{sec3-2}
Using the corpus created for citizen opinion analysis in Section \ref{sec4-1}, we constructed a classification model for each viewpoint. Figure \ref{fig:extraction}. overviews our model.

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%%%% fig. 1
\begin{figure}[t]
\begin{center}
\includegraphics{29-2ia6f1.pdf}
\end{center}
\caption{Methodology for extracting citizen opinions.}
\label{fig:extraction}
\end{figure}
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\subsubsection{Multitask Learning Method for Opinion Units}
\label{3-2-1}

In the estimation method for opinion units, we used a common BERT model to simultaneously estimate the three viewpoints (appraisal/communication opinion types and polarity types) of the opinion unit. The three viewpoints of the opinion units refer to the same opinion. Therefore, we assumed that these estimation tasks relate to each other and show their effectiveness of the multitask learning approach for estimation tasks. In this method, we used opinion units as inputs for the BERT model and obtained embeddings with 768 dimensions. We also used the embeddings from the BERT model as inputs for the linear transformation layer\footnote{$768 \times $the number of values for each viewpoint.} specific for each task. The output of the linear transformation layer was then fed to the softmax function. Finally, we obtained the prediction probabilities for the values of each viewpoint.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Estimation Method for Tweets}
\label{3-2-2}

Because a particular relationship does not exist between the viewpoints of tweets, we used the BERT model to estimate each viewpoint independently. First, we used each tweet as input for the BERT model, which was fine-tuned for each viewpoint. The output embeddings from each BERT model were fed to a linear transformation layer for each viewpoint. We then used the outputs in the softmax function to predict the value of each viewpoint. We used the output of the [CLS] token\footnote{[CLS] stands for classification. It was added at the beginning to represent the meaning of the entire sentence.} as the tweet embedding. We determined that we can attain a better accuracy using the embeddings at the [CLS] token than applying max or average pooling to all input token embeddings.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experiment: Estimation of Viewpoints}
\label{chap4}

In Section \ref{sec4-1}, 
\pagebreak
we describe the method of collecting tweets for the corpus and the results of manually annotating each viewpoint. In Section \ref{sec4-2}, we describe the experiment using the corpus to estimate each viewpoint.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Corpus for Citizen Opinion Analysis}
\label{sec4-1}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Tweet Collection}
\label{sec4-1-1}

In this experiment, we created a corpus for the two domains, nursery school and takeout services, using Japanese tweets from Yokohama citizens. First, we collected tweets from 82,583 Twitter accounts of Yokohama citizens based on their profile information using the Twitter Streaming API. From 28,971,414 tweets from January 1, 2020, to July 11, 2020, we collected tweets containing the words ``nursery school'' [{\it hoiku-en}, in Japanese (hereinafter, the Japanese equivalent expressions are in square brackets)], ``nursery teacher'' [{\it hoiku-shi}], ``childcare activities'' [{\it hokatsu}], and ``children on waiting lists'' [{\it taiki-jidou}] in Japanese for the nursery school domain and tweets containing the words ``takeout'' [{\it mochikaeri}, {\it teiku-auto}\footnote{Note that ``{\it teiku-auto}'' is expressed using {\it katakana} characters, following the conversion of foreign words.}] in Japanese for the takeout service domain. Retweets and duplicate tweets were removed.

The dataset used in this study contained 2,622 tweets common to each domain. We used spaCy,\footnote{\url{https://spacy.io/}} a Python library, to split tweets into sentences. Line breaks were used as sentence separators. Because some sentences consisted of nouns only and did not contain opinions, they were combined with the previous sentence. If they appeared as the first sentence in the tweet, they were appended to the next sentence. Furthermore, sentences with only hashtags were combined with the previous sentences because many of them did not contain opinions. Finally, sentences starting with closing parentheses were combined with previous sentences.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Annotation of Viewpoints}
\label{sec4-1-2}

We manually annotated the collected tweets with the viewpoints defined in Section \ref{sec3-1} to create a corpus for citizen opinion analysis. The annotation process was performed by five annotators, including the first author.

Each annotator judges the viewpoints to follow the agreed annotation policy. In the training phase, all five annotators judged each viewpoint for 500 sentences (237 tweets), including 250 sentences (103 tweets) in the nursery school domain and 250 sentences (134 tweets) in the takeout service domain. The training phase was completed when all annotators agreed on the annotation policy. When multiple opinions were to be included in a sentence, the sentence was segmented until multiple opinions did not appear in the opinion unit. This segmentation was performed only for sentences in which the majority of annotators agreed. 

After completing the training phase, all remaining annotation tasks were performed. The first author annotated all tweets, and the remaining four annotators were divided into two groups of two annotators. We assigned half of the tweets to each group. Therefore, all tweets were annotated by three annotators. To reiterate, if a sentence contained more than one opinion, it was segmented until at most one opinion appeared. Table \ref{tab:table1} provides the agreement of each group's annotations using the Fleiss' $\kappa$ coefficient \cite{fleiss}. The degree of agreement was 0.6 (Substantial Agreement \cite{landis1977}) or higher for all viewpoints, and the degrees of agreement between the two groups were near each other.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 1
\begin{table}[t]
\input{06table01.tex}
\caption{Agreement for each viewpoint (Fleiss' $\kappa$)}
\label{tab:table1}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The annotation results were determined by majority vote. Cases in which we could not decide the results by majority vote, because the votes tied, we discussed until agreeing on a final decision. The corpus for citizen opinion analysis consisted of 2,622 tweets and 7,916 opinion units in the nursery school and 2,622 tweets and 6,671 opinion units in the takeout service domains.

Table \ref{viewpoints} lists the construction of the viewpoints and their distributions.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 2
\begin{table}[t]
\input{06table02.tex}
\caption{Construction of viewpoints and distribution of values in the viewpoints}
\label{viewpoints}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Estimation of Viewpoints}
\label{sec4-2}

We evaluated the F1-score of each viewpoint using the model presented in Section \ref{sec3-2} and identified a suitable model for estimating each viewpoint. In this study, precision was essential for removing noisy opinions, and recall was important for avoiding missing comments from valuable citizens. In addition, when evaluating the unbalanced data in Table \ref{viewpoints}, accuracy was unsatisfactory because predicting the majority class for all items in the test data was evaluated too high.\footnote{For example, in the ``relevance to nursery school capacity'' viewpoint in the nursery school domain, predicting irrelevant labels for all tweets would result in an accuracy of 90.4\%.}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Method}
\label{sec4-2-1}

For all data in the corpus created in Section \ref{sec4-1}, we investigated the F1-score by predicting each viewpoint with the model presented in Section \ref{sec3-2} using fivefold cross-validation. In the fivefold cross-validation, 20\% of the total data were used as test data, 64\% of the total data were used as training data, and 16\% of the total data were used as validation data to determine the optimal number of epochs between 1 and 10 and avoid overfitting.

We used the Adam optimizer \cite{Adam} with a learning rate of $5e-5$ and pretrained Japanese BERT models\footnote{\url{https://github.com/cl-tohoku/bert-japanese}} for training. The maximum length of the input was 128 for all viewpoints. We fine-tuned all 12 layers of BERT during training, with a batch size of 16. In the multitask learning method, all data for each task were first converted into batches, and the batches were then shuffled and fed to the model with reference to MT-DNN \cite{mtdnn}. We used the output of the [CLS] token as the embedding of tweets and opinion units.

For data annotation in the takeout service domain, 95\% of the data for the domain relevance viewpoint were ``relevant.'' Consequently, this viewpoint is extremely unbalanced. To address this problem, we downsampled the majority of the data to half of that in the training set of the domain relevance viewpoint.

Our proposed method required approximately 1~h and 15~min for multitask learning in each domain with a single NVIDIA Tesla V100S PCIe, which had 32~GBs of GPU memory with two Intel Xeon Gold 6226R CPUs, and 754.6~GBs of RAM.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Comparison Methods}

We used two comparison methods to evaluate the effectiveness of the proposed method for estimating viewpoints for the opinion units in Section~\ref{3-2-1}. In one comparison method for estimating viewpoints for opinion units, we use each opinion unit as input for the BERT model independently prepared from each viewpoint. In another comparison method, we use a multitask learning method for all viewpoints (including viewpoints for opinion units and viewpoints for tweets), where they are learned by a single BERT model simultaneously. In this method, we use all opinion units and tweets as inputs for a single BERT model (hereinafter, the multitask learning method for all viewpoints is referred to as the ``For All Viewpoints'' method).

In addition, we used a support vector machine (SVM) and random forest (RF) with a pretrained Japanese fastText \cite{fasttext} model\footnote{\url{https://fasttext.cc/docs/en/crawl-vectors.html}} to evaluate the estimation methods using the BERT model. The hyperparameters of the SVM and RF were the default values in the scikit-learn\footnote{\url{https://scikit-learn.org/}} library. We first obtained the word embeddings using fastText and then created the embeddings of opinion units and tweets using the max and average pooling methods. However, for opinion units, embeddings could not be obtained because some words did not exist in the fastText dictionary. In this case, we regarded all predictions of the model as ``no opinion.''


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Experimental Results and Discussion}
\label{sec4-2-2}

Table \ref{tab:table2} presents the F1-scores of each viewpoint of the opinion unit. The F1-score for all viewpoints improved with the multitask learning method. In particular, the proposed method that learns using opinion units significantly improved the F1-score. The F1-score of the polarity viewpoint in the nursery school domain improved by 10\% more than the independent learning method. The higher F1-score of the proposed method, compared with the ``For All Viewpoints'' method, indicates the effectiveness of learning viewpoints related to the same opinion simultaneously. The information on the viewpoints assigned to the tweets (unrelated to the same opinion) is rather noisy for estimating opinion-related viewpoints.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 3
\begin{table}[b]
\input{06table03.tex}
\hangcaption{F1-scores of each viewpoint (opinion unit). ``**'' indicates that the difference between our proposed method and comparison methods in Macro average is statistically significant for $p<0.01$, using a two-tailed paired samples \textit{t}-tests.}
\label{tab:table2}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 4
\begin{table}[b]
\input{06table04.tex}
\hangcaption{F1-scores of each viewpoint (tweet). ``*'' indicates that the difference between the BERT model and comparison methods in Macro average is statistically significant for $p<0.05$, using a two-tailed paired samples \textit{t}-tests.}
\label{tab:table3}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Table \ref{tab:table3} lists the F1-scores of each viewpoint for tweets. Although the estimation of viewpoints for tweets using the BERT model is acceptable with a high F1-score, the F1-score for the status of citizen is low in both domains. In the nursery school domain, where the F1-score is relatively low, many examples were observed of incorrectly estimating the status of citizen viewpoint. For example, a parent citizen who said ``I envy you'' after describing the responses of other nursery schools was classified as ``others.''  Estimating the status of citizen in such detailed contexts is limited when using only one tweet. We plan to improve the F1-score using the username \cite{wood_2018} and profile description as additional features that indicate the citizen status such as ``nursery teacher'' or ``mother of two children'' in a future study. Presently, we propose implementing a framework for using this information to categorize accounts in advance.

In this study, we used the F1-score as an evaluation measure. For imbalanced data with a small number of relevant (positive instance class) data as ``relevant to a specific'' viewpoint, the F1-score is lower. Moreover, for imbalanced data with a small number of irrelevant (negative instance class) data as ``domain relevance'' viewpoint, the F1-score tends to be higher. However, the F1-score of the ``relevance to school closures and restraint of school attendance'' viewpoint, is relatively high because the tweet contains some explicit clues, such as ``school closure,'' ``restraints on attending schools,'' ``working at home,'' and ``childcare leave.'' Despite the limited relevant data, the F1-score of the ``regional dependency'' viewpoint in the nursery school domain was relatively high because the tweets also contained explicit clues, such as the name of specific regions or facilities.

We also tried multitask learning methods (the ``For All Viewpoints'' method has all viewpoints {\bf for tweets and opinion units} are learned simultaneously by a single BERT model; the ``For Tweets'' method has all viewpoints {\bf for tweets} learned simultaneously) for viewpoints for tweets but did not find consistent effectiveness.

In the multitask learning method, the performance of each task can be improved using strongly related tasks \cite{multitask}. When we investigated the distribution of the values of the ``polarity'' viewpoint for the values of the ``appraisal opinion type'' viewpoint, the rate of ``negative'' labels, such as dissatisfaction and anxiety, was relatively high in the ``expressions of feelings'' value in both domains. Moreover, in the ``judgment opinions for the behavior or actions of a person or organization'' value, the rate of ``positive'' labels, such as praise for the work of nursery school staff and gratitude for people who used the stores by restaurant staff, was relatively higher than that in the ``emotion expression'' value. Furthermore, ``no opinion'' labels were shared between the ``appraisal opinion type'' and ``polarity'' viewpoints. 
In addition, the attitudes of appraisal theory included both positive and negative opinions \cite{appraisal}. In the communication opinion type viewpoint, the ``neutral'' label of the ``polarity'' viewpoint accounts for most of the communication opinion type. Therefore, a strong relatedness was observed among the estimation tasks of the three viewpoints of the opinion unit; therefore, multitask learning improved the F1-scores. However, for tweet viewpoints, the relatedness among the tasks was weaker than that among the viewpoints for opinion units, because the distribution of the viewpoints for tweets had minimal association. Therefore, the multitask learning method did not improve the F1-scores, as expected.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Model's Performance in Domain Transfer}
\label{sec4-2-4}

In this section, we discuss the performance of the proposed method for transferring the domain in estimating opinion unit viewpoints. As additional annotations are required to extract opinions in new domains, we examined the effectiveness of the proposed method in domain transfer to reduce costs and investigate how much annotation data are required to extract opinions in new domains. We tested a two-stage fine-tuning method, where the model was fine-tuned once using all datasets in the source domain, and fine-tuned again using datasets from the target domain.

This method validates the amount of data required to estimate the viewpoints of a target domain. The amount of data from the target domain was adjusted to 0\%, 25\%, 50\%, 75\%, and 100\%. When the amount of data in the target domain was 0\%, the model was fine-tuned using only data from the source domain. When the target domain was between 25\% and 100\%, we performed the fivefold cross-validation, as explained in Section \ref{sec4-2-1}, and adjusted the amount of training and validation data between 25\% and 100\%; however, we still used the same test data as in Section \ref{sec4-2-1}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 5
\begin{table}[b]
\input{06table05.tex}
\caption{F1-score of each viewpoint (opinion unit) using data from different domains}
\label{tab:transfer}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Table \ref{tab:transfer} presents the results. Using 25\% of the data from the target domain yields a better performance than the independent BERT model using only the source domain data, except for the appraisal and communication opinion type viewpoints in the nursery school domain. In addition, relatively high F1-scores were obtained when the viewpoints of the takeout service domain were estimated using data from the nursery school domain.  We assume that the nursery school domain contains more diverse opinions than the
takeout service domain, as shown in Table \ref{viewpoints}, easing the adaptation of the target domain.\footnote{To clarify that the effect is not because of the number of query keywords collecting tweets, we investigated the tweets containing only one query keyword, ``nursery school'' [{\it hoiku-en}], among the four search keywords in the nursery school domain. Consequently, the distribution of the ``appraisal opinion type'' viewpoint was similar to using all search keywords.}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Chronological Analysis of Citizens' Opinions by Appraisal Opinion Types}

To verify the usefulness of estimating appraisal opinion types, we discuss the effectiveness of extracting citizen feedback by appraisal opinion types with the chronological transition analysis of citizens' tweets during the COVID-19 pandemic.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Method}
\label{sec5-1}

The estimated results of appraisal opinion types using the additional test data in Section \ref{sec5-2} were arranged in chronological order by the frequency of occurrence for 10 days each, and the trends of the frequency of occurrence for each time span were analyzed. The topics of the tweets used for the chronological analysis are tweets relevant to school closures, school attendance restraint in the nursery school domain, and all tweets in the takeout service domain. We also investigated how different opinions can be extracted by changing the appraisal opinion type and related viewpoints.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Additional Test Data}
\label{sec5-2}

In addition to the tweets included in the created corpus for citizen opinion analysis, we collected tweets from January 1, 2020, to July 11, 2020, in the same manner as in Section \ref{sec4-1-1}, to expand the tweets for chronological analysis and citizen feedback extraction. The total number of tweets crawled between January 1 and July 11 was 9,535 in the nursery school domain, and 21,014 in the takeout service domain. For the corpus created for citizen opinion analysis in Section \ref{sec4-1-1}, we randomly sampled 2,622 tweets in both domains to match the number of tweets according to the annotator's employment time. For additional test data, we selected 6,913 tweets in the nursery school domain and 18,392 tweets in the takeout service domain, which did not overlap with the tweets in Section \ref{sec4-1-1}. The number of sentences obtained from the newly collected tweets was 22,256 in the nursery school domain and 54,772 in the takeout service domain. We used the newly collected tweets and test data in the fivefold cross-validation of the experiment described in Section \ref{sec4-2-1} as additional test data. We estimated the viewpoints for these tweets and sentences using the classification model for each viewpoint trained on the training data of the corpus for the citizen opinion analysis in Section \ref{sec4-2-1} as supervisory data.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 2
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia6f2.pdf}
\end{center}
\hangcaption{Chronological transition of appraisal opinion types in relevance to school closures and school attendance restraint in the nursery school domain.}
\label{hoiku_freq}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Discussion}
\label{sec5-3}

Figure \ref{hoiku_freq} shows the frequency of opinions in chronological order regarding nursery school closures and attendance restraint in nursery schools. It shows that the number of ``expression of feelings'' such as fear of COVID-19 and dissatisfaction with the government is high. In addition, the number of opinions increased sharply in late February, early April, and late May. Therefore, we checked the top frequency of the n-grams ($n=4$) of tweets during these periods.  First, in late February, the frequency of the n-gram ``{\it hoiku-en mo yasumi ni} (please nursery schools also closed)'' ranked first, and ``{\it hoiku-en mo kyuu-en ni} (please nursery schools also closed)'' ranked second.  The word ``{\it mo} (also),'' a Japanese particle, indicates that citizens compared nursery schools to other closed facilities. Therefore, we checked tweets including ``{\it hoiku-en mo yasumi ni} (please nursery schools also closed)'' and found that citizens mentioned that elementary and junior high schools were closed. In addition, when we checked Yokohama City's policy, we found that ``(1) the closure of elementary and junior high schools because of COVID-19 in late February'' was announced by Yokohama City in this time period. In early April, the frequency of the n-gram ``{\it tou enn jisyuku shi} (restraint on attending nursery school)'' was ranked first, and the n-gram ``{\it hoiku-en ga kyuu-en ni} (the nursery school was not closed/if the nursery school was closed)''\footnote{When we checked tweets including this n-gram, we found three negations (two ``{\it ni nara nai}'' and one ``{\it ni na tta wake ja nai}'') and seven subjunctive moods (``{\it ni na tta ra},'' ``{\it ni na ttari shi te},'' etc.) among 10 tweets.} ranked second. Therefore, we checked the tweets containing these n-grams and found that the citizens mentioned that Yokohama City did not close nursery schools; that is, the city asked parents to refrain from having children attend nursery school. To reiterate, when we checked Yokohama City's policy, we found that ``(2) request to exercise restraint regarding children attending nursery schools'' was announced by Yokohama City at this time. Finally, in late May, n-grams including ``{\it 6gatsu matsu} (end of June)'' or ``{\it 6 / 30} (June 30)'' ranked among the top 10 in n-gram frequency of occurrences four times. Therefore, we checked tweets that contained these n-grams and found that the citizens of Yokohama City mentioned that they refrained from having children attend nursery schools until the end of June. When we checked Yokohama City's policy, we found that the ``(3) plan to exercise restraint regarding children attending nursery schools until the end of June'' was announced by Yokohama City at this time. In this manner, we can detect social events from citizens' tweets and extract citizens' feedback about these events using our approach.

In early April, if we extract citizens' opinions in the nursery school domain by specifying the viewpoints as the appraisal opinion type with the ``expression of feelings'', status of citizen with the ``parents with small children'', domain relevance with the ``relevant'', and regional dependency with the ``dependent'' values, we can understand the emotions and opinions of parents raising children in Yokohama City regarding sending children to nursery schools during the COVID-19 pandemic. The results show that parents in Yokohama City were anxious about sending their children to nursery schools; that is, they held negative feelings toward the local government's policies. In addition, \citeA{Kido2021} investigated the type of behavior from the nursery staff that would reassure the parents during the COVID-19 crisis. Thus, this is valuable for clarifying information from the citizens' tweets. To extract these citizens' opinions, we changed the viewpoints from the appraisal opinion type to ``judgment opinion for behavior or actions of a person or organization,'' the polarity to ``positive,'' and does not specify a regional dependency. Consequently, we can extract best practice information on how nursery schools should have handled COVID-19 issues in early April, such as ``The nursery staff member wears a hat, glasses, mask, and gloves on her way to work, and she changes all of her clothes before going to work \includegraphics{29-2ia6_53-1.pdf}. I'm really grateful for their appropriate setup for anti-COVID-19 measures.'' This information is extremely useful in unprecedented situations.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 3
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia6f3.pdf}
\end{center}
\hangcaption{Chronological transition of communication opinion types in relevance to school closures and school attendance restraint in the nursery school domain.}
\label{hoiku_freq_2}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Figure \ref{hoiku_freq_2} shows the frequency of communication opinions in chronological order regarding nursery school closures and attendance restraint in nursery schools. This shows that the rate of citizens' speculation was higher in late February, when COVID-19 began to spread in Japan. If we extract citizens' opinions in the nursery school domain at this time by specifying the viewpoints with a communication opinion type of ``speculation,'' status of citizen as ``parents with small children,'' domain relevance as ``relevant,'' and relevance to school closures and restraint of school attendance as ``relevant,'' we can extract citizens' uncertain speculations, such as ``The nursery staff are also parents, so the nursery school will probably be closed because they cannot care for their children.'' The rate of ``speculation'' opinions, however, became relatively lower compared with ``question'' or ``request'' types following the spread of COVID-19 and declaration of a state of emergency. Furthermore, citizens tweeted their ``suggestion'' opinions in early April, when the state of emergency was declared. If we specify the communication opinion type viewpoint with the ``suggestion'' value at this time, we can extract citizens' suggestions, such as ``Licensed nursery schools should be closed to children, even for one or two weeks.'' As the government must make policies that reflect citizens' opinions during unprecedented situations, these suggestions serve as valuable information.

Figure \ref{Takeout_freq} shows the frequency of the occurrence of opinions in chronological order regarding restaurant takeout services. The number of opinions increased sharply in early April when Japan's COVID-19 pandemic reached its maximum number of daily new cases, and many restaurants began to offer takeout services, which peaked in late April; the number of opinions decreased when the emergency declaration ended and many restaurants resumed normal business in late May. Furthermore, unlike the nursery school domain, we found that the number of ``evaluative opinions directed at a thing or event,'' such as ``tastes authentic,'' ``fresh,'' and ``luxurious,'' was high. Using the appraisal opinion type viewpoint, we can see that each domain has different characteristic opinions.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 4
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia6f4.pdf}
\end{center}
\caption{Chronological transition of appraisal opinion types in the takeout service domain.}
\label{Takeout_freq}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

In late April, if we extract citizens' opinions in the takeout service domain by specifying the viewpoints in the appraisal opinion type as ``evaluative opinion directed at a thing or event,'' status of citizen as ``people who used the stores,'' polarity as ``positive,'' domain relevance as ``relevant,'' regional dependency as ``dependent,'' and relevance to product aspect as ``relevant,'' we can understand information such as highly rated reviews by people who actually used the takeout service of a restaurant. In addition, \citeA{hashimoto2021} investigated the advantages and disadvantages of using takeout services to improve the service of restaurant staff during COVID-19. Therefore, to clarify this information by extracting citizens' opinions from the tweets, we changed the viewpoints from the appraisal opinion type to ``judgment opinion for behavior or actions of a person or organization'' and did not specify the relevance to the product aspect. Consequently, we can extract opinions on the behavior of the restaurant staff that users feel comfortable with, such as ``It's freshly cooked because it's cooked after you order. 540 yen including tax for a set of salad and a mask is a bargain, right?'' This is particularly important when restaurant staff test their takeout service. According to \citeA{hashimoto2021}, the disadvantages of takeout services include a lack of freshness and cost. Therefore, the citizen comments we extracted validated their investigation.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusion}

We proposed a method for assigning multiple viewpoints to tweets and extracting citizens' opinions by specifying the conditions of the viewpoints.  We proved that the F1-score for estimating the values of viewpoints on opinion units could be improved with multitask learning. By investigating chronological opinion transitions based on appraisal opinion types, we can detect social events from citizens' tweets. In addition, by changing appraisal opinion types and related viewpoints, we found the possibility of extracting valuable citizen feedback, such as feelings about local government policies and positive judgment opinions about people's best practices.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\acknowledgment

This work was partially supported by the Japanese Society for the Promotion of Science Grant-in-Aid for Scientific Research (B) (\#19H04420).


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\bibliographystyle{jnlpbbl_1.6}
\bibliography{06refs}


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\begin{biography}

\bioauthor[:]{Tetsuya Ishida}{
He received his B.A. degree in Library and Information Science from University of Tsukuba in 2021. He is currently a Master's student at Master's Program in Informatics, Graduate School of Comprehensive Human Sciences, University of Tsukuba. His research interests include natural language processing and sentiment analysis.
}

\bioauthor[:]{Yohei Seki}{
He  received  his  M.S.  degree  in  Computer  Science  from  Keio
University in 1996 and his Ph.D. degree in Informatics from the Graduate University for Advanced Studies (SOKENDAI) in 2005. He was an assistant professor at Toyohashi University of Technology and a visiting scholar at Columbia University in 2008 and at National University of Singapore in 2018, respectively. He is currently an associate professor at University of Tsukuba. His main research interests are natural language processing, sentiment analysis, and information access.
}

\bioauthor[:]{Wakako Kashino}{
She received her B.A. degree in Japanese Literature from Tokyo
Woman's Christian University in 1991 and Ph.D. degree from Tokyo Institute of Technology in 2016. She is currently an associate professor at National Institute for Japanese Language and Linguistics. Her research interests include Japanese corpus linguistics and lexicology.
}

\bioauthor[:]{Noriko Kando}{
She received her Ph.D. from Keio University in Library and Information Science in 1995 and has been teaching and conducting research at National Institute of Informatics (NII) since 1994. She is currently a professor at the Information-Society Research Division of the National Institute of Informatics (NII), and has also been appointed as a professor in the Department of Informatics at the Graduate University of Advanced Studies (SOKENDAI). She initiated NTCIR in late 1997, an evaluation of information-access technologies, such as information retrieval, summarization, question answering, sentiment and argument analysis, and text mining, using various types of documents in East Asian languages and English, and has been a designer of various tasks and general co-chair of NTCIR. She is honored as the SIGIR Academy inductee.
}

\end{biography}


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\biodate



\end{document}

