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Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Authors:

Jiebo LuoAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 117 - 125

https://doi.org/10.1145/3240508.3240533

Published: 15 October 2018 Publication History

Abstract

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

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Index Terms

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

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Information

Published In

cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

Copyright © 2018 ACM.

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Published: 15 October 2018

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National Science Foundation
Goergen Institute of Data Science

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MM '18

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MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Alawneh HHasasneh AMaree M(2024)On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment AnalysisModelling10.3390/modelling50400765:4(1469-1489)Online publication date: 7-Oct-2024
https://doi.org/10.3390/modelling5040076
Khalil AAwan MYasin AKousar TRahman AYoussef M(2024)Hybrid Time Series Model for Advanced Predictive Analysis in COVID-19 VaccinationElectronics10.3390/electronics1313246813:13(2468)Online publication date: 24-Jun-2024
https://doi.org/10.3390/electronics13132468
Levi SHauthal EMukherjee SOstermann F(2024)Visualizing emoji usage in geo-social media across time, space, and topicFrontiers in Communication10.3389/fcomm.2024.13036299Online publication date: 17-Jan-2024
https://doi.org/10.3389/fcomm.2024.1303629
Huang JLi XLi QDu YFan YChen XHuang DWang S(2024)Incorporating emoji sentiment information into a pre-trained language model for Chinese and English sentiment analysisIntelligent Data Analysis10.3233/IDA-23086428:6(1601-1625)Online publication date: 15-Nov-2024
https://doi.org/10.3233/IDA-230864
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ZUO XCHEN YHärdle W(2024)Emoji Driven Crypto Assets Market ReactionsSSRN Electronic Journal10.2139/ssrn.4722627Online publication date: 2024
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Dubé SGesselman AKaufman EBennett-Brown MTa-Johnson VGarcia J(2024)Beyond words: Relationships between emoji use, attachment style, and emotional intelligencePLOS ONE10.1371/journal.pone.030888019:12(e0308880)Online publication date: 6-Dec-2024
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Liu ZLiao HLi MYang QMeng F(2024)A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term SetsIEEE Transactions on Engineering Management10.1109/TEM.2023.3271597(1-18)Online publication date: 2024
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Lee SKim SChu YChoi JPark EWoo S(2024)EAE-GAN: Emotion-Aware Emoji Generative Adversarial Network for Computational Modeling Diverse and Fine-Grained Human EmotionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332943411:3(3862-3872)Online publication date: Jun-2024
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