Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network

Li Yang; Jianfei Yu; Chengzhi Zhang; Jin Cheon Na

doi:10.1007/978-3-030-71292-1_31

Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network

Li Yang, Jianfei Yu^*, Chengzhi Zhang, Jin Cheon Na

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Citations (Scopus)

Abstract

Fine-grained sentiment analysis of social platforms like Twitter and Facebook nowadays becomes increasingly important, as it can reflect public opinions towards target entities such as politicians. Entity-Level Sentiment Analysis (ELSA) is an important fine-grained SA task, aiming to identify the sentiment over each entity mentioned in a sentence. Most previous methods to this task primarily rely on the text, but ignoring the other useful multimodal data sources (e.g., images). Therefore, in this paper, we aim to explore the usefulness of associated images for ELSA in multimodal tweets (especially political tweets). Specifically, we propose an Entity-Aware Multimodal Network (EAMN), and apply it to political tweets for understanding public opinions towards some politicians. Experiment results show that the associated images are generally useful for ELSA, and our EAMN model achieves the state-of-the-art results on two public Twitter datasets and our political Twitter datasets.

Original language	English
Title of host publication	Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings
Editors	Katharina Toeppe, Hui Yan, Samuel Kai Chu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	411-420
Number of pages	10
ISBN (Print)	9783030712914
DOIs	https://doi.org/10.1007/978-3-030-71292-1_31
Publication status	Published - 2021
Externally published	Yes
Event	16th International Conference on Diversity, Divergence, Dialogue, iConference 2021 - Beijing, China Duration: Mar 17 2021 → Mar 31 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12645 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Diversity, Divergence, Dialogue, iConference 2021
Country/Territory	China
City	Beijing
Period	3/17/21 → 3/31/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Fine-grained sentiment analysis
Multimodal sentiment analysis
Opinion mining
Political tweets

Access to Document

10.1007/978-3-030-71292-1_31

Cite this

Yang, L., Yu, J., Zhang, C., & Na, J. C. (2021). Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network. In K. Toeppe, H. Yan, & S. K. Chu (Eds.), Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings (pp. 411-420). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12645 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-71292-1_31

Yang, Li ; Yu, Jianfei ; Zhang, Chengzhi et al. / Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network. Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings. editor / Katharina Toeppe ; Hui Yan ; Samuel Kai Chu. Springer Science and Business Media Deutschland GmbH, 2021. pp. 411-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{202a1aeb7b474a88b304f2f3a773fb93,

title = "Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network",

abstract = "Fine-grained sentiment analysis of social platforms like Twitter and Facebook nowadays becomes increasingly important, as it can reflect public opinions towards target entities such as politicians. Entity-Level Sentiment Analysis (ELSA) is an important fine-grained SA task, aiming to identify the sentiment over each entity mentioned in a sentence. Most previous methods to this task primarily rely on the text, but ignoring the other useful multimodal data sources (e.g., images). Therefore, in this paper, we aim to explore the usefulness of associated images for ELSA in multimodal tweets (especially political tweets). Specifically, we propose an Entity-Aware Multimodal Network (EAMN), and apply it to political tweets for understanding public opinions towards some politicians. Experiment results show that the associated images are generally useful for ELSA, and our EAMN model achieves the state-of-the-art results on two public Twitter datasets and our political Twitter datasets.",

keywords = "Fine-grained sentiment analysis, Multimodal sentiment analysis, Opinion mining, Political tweets",

author = "Li Yang and Jianfei Yu and Chengzhi Zhang and Na, {Jin Cheon}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 16th International Conference on Diversity, Divergence, Dialogue, iConference 2021 ; Conference date: 17-03-2021 Through 31-03-2021",

year = "2021",

doi = "10.1007/978-3-030-71292-1_31",

language = "English",

isbn = "9783030712914",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "411--420",

editor = "Katharina Toeppe and Hui Yan and Chu, {Samuel Kai}",

booktitle = "Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings",

address = "Germany",

}

Yang, L, Yu, J, Zhang, C & Na, JC 2021, Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network. in K Toeppe, H Yan & SK Chu (eds), Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12645 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 411-420, 16th International Conference on Diversity, Divergence, Dialogue, iConference 2021, Beijing, China, 3/17/21. https://doi.org/10.1007/978-3-030-71292-1_31

Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network. / Yang, Li; Yu, Jianfei; Zhang, Chengzhi et al.
Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings. ed. / Katharina Toeppe; Hui Yan; Samuel Kai Chu. Springer Science and Business Media Deutschland GmbH, 2021. p. 411-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12645 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network

AU - Yang, Li

AU - Yu, Jianfei

AU - Zhang, Chengzhi

AU - Na, Jin Cheon

PY - 2021

Y1 - 2021

N2 - Fine-grained sentiment analysis of social platforms like Twitter and Facebook nowadays becomes increasingly important, as it can reflect public opinions towards target entities such as politicians. Entity-Level Sentiment Analysis (ELSA) is an important fine-grained SA task, aiming to identify the sentiment over each entity mentioned in a sentence. Most previous methods to this task primarily rely on the text, but ignoring the other useful multimodal data sources (e.g., images). Therefore, in this paper, we aim to explore the usefulness of associated images for ELSA in multimodal tweets (especially political tweets). Specifically, we propose an Entity-Aware Multimodal Network (EAMN), and apply it to political tweets for understanding public opinions towards some politicians. Experiment results show that the associated images are generally useful for ELSA, and our EAMN model achieves the state-of-the-art results on two public Twitter datasets and our political Twitter datasets.

AB - Fine-grained sentiment analysis of social platforms like Twitter and Facebook nowadays becomes increasingly important, as it can reflect public opinions towards target entities such as politicians. Entity-Level Sentiment Analysis (ELSA) is an important fine-grained SA task, aiming to identify the sentiment over each entity mentioned in a sentence. Most previous methods to this task primarily rely on the text, but ignoring the other useful multimodal data sources (e.g., images). Therefore, in this paper, we aim to explore the usefulness of associated images for ELSA in multimodal tweets (especially political tweets). Specifically, we propose an Entity-Aware Multimodal Network (EAMN), and apply it to political tweets for understanding public opinions towards some politicians. Experiment results show that the associated images are generally useful for ELSA, and our EAMN model achieves the state-of-the-art results on two public Twitter datasets and our political Twitter datasets.

KW - Fine-grained sentiment analysis

KW - Multimodal sentiment analysis

KW - Opinion mining

KW - Political tweets

UR - http://www.scopus.com/inward/record.url?scp=85104874934&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85104874934&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-71292-1_31

DO - 10.1007/978-3-030-71292-1_31

M3 - Conference contribution

AN - SCOPUS:85104874934

SN - 9783030712914

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 411

EP - 420

BT - Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings

A2 - Toeppe, Katharina

A2 - Yan, Hui

A2 - Chu, Samuel Kai

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

Y2 - 17 March 2021 through 31 March 2021

ER -

Yang L, Yu J, Zhang C, Na JC. Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network. In Toeppe K, Yan H, Chu SK, editors, Diversity, Divergence, Dialogue - 16th International Conference, iConference 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 411-420. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-71292-1_31

Fine-Grained Sentiment Analysis of Political Tweets with Entity-Aware Multimodal Network

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this