Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction (2021)

Abstract: Conventional opinion polls were usually conducted via questionnaires or phone interviews, which are time-consuming and error-prone. With the advances in social networking platforms, it’s easier for the general public to express their opinions on popular topics. Given the huge amount of user opinions, it would be useful if we can automatically collect and aggregate the overall topical stance for a specific topic. In this paper, we propose to predict topical stances from social media by concept expansion, sentiment classification, and stance aggregation based on word embeddings. For concept expansion of a given topic, related posts are collected from social media and clustered by word embeddings. Then, major keywords are extracted by word segmentation and named entity recognition methods. For sentiment classification and aggregation, machine learning methods are used to train sentiment lexicon with word embeddings. Then, the sentiment scores from user-centric and post-centric views are aggregated as the total stance on the topic. In the experiments, we evaluated the performance of our proposed approach using social media data from online forums. The experimental results for 2016 Taiwan Presidential Election showed that our proposed method can effectively expand keywords and aggregate topical stances from the public for accurate prediction of election results. The best performance is 0.52% in terms of mean absolute error (MAE). Further investigation is needed to evaluate the performance of the proposed method in larger scales.

## introduction people usually express their opinions in social occasions with friends and to the public. to know what the general public think about a specific topic, it usually takes much human efforts in designing questionnaires, collecting feedbacks and analyzing them. it's time-consuming and error prone. depending on the participation of people, there could be not too many effective responses. with the advances of social networking platforms, it's very easy to post articles and reply with comments. for example, twitter, facebook, and instagram are among the most popular social networking sites with different functions. this facilitates users to make online discussions in an immediate way. given huge amount of social opinions, it would be useful if we can automatically collect and aggregate the general stances from them. there are some challenges to the problem. firstly, given the very diverse contents in social media, it would be difficult to obtain the most relevant contents from huge amount of data. secondly, people might express their opinions in different ways. it would be difficult to extract what they really think about specific topics from very short texts in social media. content in a short text is usually limited in scope. without explaining the ideas and referencing related documents, we might only obtain fragmented terms or named entities just from the sole content of a single post. it might even contain emotional feelings or noises that cannot help us clarify the main idea. on the other hand, users have different types of activities in addition to posting. for example, most social networking platforms provide mechanisms for making friends, following people or topics that you are interested in, and expressing agreement or disagreement, replying, or commenting on others' posts. these social relations, both explicit and implicit, provide useful clues for understanding what people really think, in addition to what they explicitly mention in post contents. this makes it possible to analyze user opinions by extracting social relations and discovering the major concepts. in this paper, we propose to aggregate usercentric and post-centric stances for topical stance prediction. firstly, given the simple topical keyword, we expand the concepts by clustering topic-related posts and comments by their word embeddings, and extract major keywords from each group using word segmentation and named entity recognition methods. then, given word embeddings, sentiment classification is done by machine learning methods including naïve bayes (nb) and extreme learning machines (elms) @xcite . finally, we aggregate topical stances using both post-centric and user-centric sentiments. in post-centric views, the more positive feedbacks a post gets, the more positive it is regarding the topic. in user-centric views, the more positive comments a user gives, the more positive the user is regarding the topic. by aggregating both post-centric and user-centric sentiments, we are able to analyze the influences of user posts from broader aspects. in the experiments, we collected data from the most popular online discussion forum in taiwan called ptt. for sentiment analysis on short texts, we found inconsistent sentiment between user ratings and post contents. after adjustment, elms are more stable in sentiment classification performance than naïve bayes classifiers. by aggregating stances on three groups of candidates in the 2016 taiwan presidential election to predict the election result, the best performance can be obtained for elms with the mae of 0.52%. this shows the potential of our proposed approach in stance prediction. further investigation is needed for different types of social media in larger scales. ## related work sentiment classification is one of the major techniques for social media analysis and opinion mining. documents are classified by overall sentiment instead of topic. for example, @xcite first utilized machine learning techniques in learning classifiers for positive and negative movie reviews. they found features as important factors in social media sentiment classification. conventional bag-of-words models do not distinguish between word orders. word n-gram models such as bigrams simply consider consecutive words as a unit for representing documents. it's only limited in the local context of words. nowadays, word embedding models such as word2vec @xcite or glove @xcite have been used as a more suitable representation of documents, especially for short texts in social media. they utilize neural networks to learn the semantics of words in different contexts. furthermore, different deep learning methods have been used to automatically learn the features in sentiment classification. for example, recurrent neural networks (rnns) and their variants such as long short-term memory (lstm) and gated recurrent units (grus) are often used to capture long-term dependency in sequential data. they have been successfully applied in sentiment classification of tweets @xcite . convolutional neural networks (cnns) were originally used in image recognition. with suitable representation of word embeddings in documents, cnns were also found effective in sentiment classification of tweets @xcite . based on sentiment classification of a single review or post, it's useful to further determine the stance that indicates whether the author is in favor of or against a specific target entity. for example, @xcite created the first stance dataset in twitter, and proposed a stance detection system using support vector machine (svm) classifiers with character and word n-grams and word embedding features. but the target entity needs to be specified before determining the stance, and it's only based on the tweet content. it's closely related to aspect-based sentiment analysis tasks in semeval 2014 @xcite and semeval 2015 @xcite . in addition to the typical stance detection of texts, social media data are often used in determining the polarization in political opinions @xcite and predicting voting intentions or outcomes in elections @xcite . instead of detecting the stance of a single user on a specific target, it's useful to derive the stance from the general public on a topic, which we called topical stance. for example, in semeval 2016 topical stance detection contest, mitre @xcite used lstm with word2vec word embeddings. deepstance @xcite used cnn models, while @xcite used attention models. @xcite developed a two-phase solution to topical stance detection for twitter including subjectivity detection and sentiment classification using lstm with attention. @xcite proposed user-level stance detection using the 33rd conference on computational linguistics and speech processing (rocling 2021) taoyuan, taiwan, october 15-16, 2021. the association for computational linguistics and chinese language processing only a few tweets for users by fine-tuning contextualized embedding. as mentioned in the literature (aldaye and magdy, 2021), there are usually two levels of stance detection: statementlevel, which is simply based on text content, and user-level, which is to predict the stance of a user on the target. also, there could be three different types of stance detection according to targets: target-specific stance, multi-related target stance, and claim-based stance. in addition to stance detection, research on stance prediction is usually concerned with detecting stances before the event. most previous studies investigated the micro-level prediction, which estimates the individual user's viewpoint toward a target. for example, @xcite considers joint modeling of content and social interactions for user stance prediction. darwish et al. ( 2017 ) used content and user interactions to calculate user similarity for stance prediction. in this paper, we propose a macro-level approach to stance prediction by aggregating topical stances from post-centric and user-centric views in social media, based on sentiment classification results on very short texts using word embedding features and machine learning methods. ## the proposed method there are three major modules in the proposed approach: concept expansion, opinion analysis, and stance aggregation. the overall architecture of the proposed approach is illustrated in fig. 1 : as a preprocessing step, given the topical keyword, topic-relevant posts and comments are collected and represented by word embeddings. first, concept expansion is done by clustering the topic-relevant posts and comments, and extracting the keyphrases in each cluster. then, machine learning methods are utilized to train the classifiers for sentiment classification. sentiment orientation is then used to calculate the corresponding stances from both post-centric and user-centric points of view. finally, the stances are aggregated by their linear combination. in the following subsections, we will explain the details. ## experiments in our experiments, we designed our customized crawler in telnet to collect data from the most popular online discussion forum called ptt. during feb. 2015 an jun. 2016, a total of 881,322 documents in chinese was collected in the discussion board of "gossip". the number of users participated in these posts is 60,018. ## discussions from our experimental results, there are some observations: firstly, from our observations on sentiment classification of ptt data, we found type-1 and type-2 errors that frequently occurred in posts. users might give positive ratings with negative contents, or vice versa. after adjusting these errors, the performance of sentiment classification can be improved for both elm and nb. secondly, we consider two different views of stance detection: post-centric and user-centric. user-centric stance detection works better than post-centric, especially for naïve bayes. finally, we validated the effects of stance aggregation by the weighted sum of both user-centric and post-centric stances. the best prediction performance with mae of 0.52% can be obtained. it shows the potential of our proposed approach to stance prediction. ## conclusions in this paper, we have proposed to aggregate postcentric and user-centric sentiments from social media for stance detection. firstly, we performed concept expansion to obtain the related concepts for the given topic. secondly, we trained classifiers such as naïve bayes and extreme learning machines for sentiment classification. finally, we proposed a potential way of calculating the individual influences from comments for posts and posts from each user, and aggregating to obtain the total stance for the topic. from our experimental results, we can see a good performance with the best mae of 0.52% when we aggregate stances estimated using elms. this shows the potential of our proposed approach in topic-specific opinion mining and stance detection. further investigations are needed to evaluate our proposed approach in different topic domains.