@inproceedings{wang-chen-2021-aggregating,
title = "Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction",
author = "Wang, Jenq-Haur and
Chen, Kuan-Ting",
editor = "Lee, Lung-Hao and
Chang, Chia-Hui and
Chen, Kuan-Yu",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.29",
pages = "226--235",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-chen-2021-aggregating">
<titleInfo>
<title>Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jenq-Haur</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kuan-Ting</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lung-Hao</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chia-Hui</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kuan-Yu</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
<place>
<placeTerm type="text">Taoyuan, Taiwan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">wang-chen-2021-aggregating</identifier>
<location>
<url>https://aclanthology.org/2021.rocling-1.29</url>
</location>
<part>
<date>2021-10</date>
<extent unit="page">
<start>226</start>
<end>235</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction
%A Wang, Jenq-Haur
%A Chen, Kuan-Ting
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F wang-chen-2021-aggregating
%X 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.
%U https://aclanthology.org/2021.rocling-1.29
%P 226-235
Markdown (Informal)
[Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction](https://aclanthology.org/2021.rocling-1.29) (Wang & Chen, ROCLING 2021)
ACL