Abstract

Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social media data present challenges to traditional sentiment analysis. First, social media data is often noisy, incomplete and fast-evolved which necessitates the design of a sophisticated learning model. Second, sentiment labels are hard to collect which further exacerbates the problem by not being able to discriminate sentiment polarities. Meanwhile, opportunities are also unequivocally presented. Social media contains rich sources of sentiment signals in textual terms and user interactions, which could be helpful in sentiment analysis. While there are some attempts to leverage implicit sentiment signals in positive user interactions, little attention is paid on signed social networks with both positive and negative links. The availability of signed social networks motivates us to investigate if negative links also contain useful sentiment signals. In this paper, we study a novel problem of unsupervised sentiment analysis with signed social networks. In particular, we incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model SignedSenti for unsupervised sentiment analysis. Empirical experiments on two real-world datasets corroborate its effectiveness.

Original languageEnglish (US)
Title of host publication31st AAAI Conference on Artificial Intelligence, AAAI 2017
PublisherAAAI press
Pages3429-3435
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

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ASJC Scopus subject areas

  • Artificial Intelligence

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Cheng, K., Li, J., Tang, J., & Liu, H. (2017). Unsupervised sentiment analysis with signed social networks. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3429-3435). AAAI press.

Unsupervised sentiment analysis with signed social networks. / Cheng, Kewei; Li, Jundong; Tang, Jiliang; Liu, Huan.

31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017. p. 3429-3435.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cheng, K, Li, J, Tang, J & Liu, H 2017, Unsupervised sentiment analysis with signed social networks. in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, pp. 3429-3435, 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States, 2/4/17.
Cheng K, Li J, Tang J, Liu H. Unsupervised sentiment analysis with signed social networks. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press. 2017. p. 3429-3435
Cheng, Kewei ; Li, Jundong ; Tang, Jiliang ; Liu, Huan. / Unsupervised sentiment analysis with signed social networks. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017. pp. 3429-3435
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abstract = "Huge volumes of opinion-rich data is user-generated in social media at an unprecedented rate, easing the analysis of individual and public sentiments. Sentiment analysis has shown to be useful in probing and understanding emotions, expressions and attitudes in the text. However, the distinct characteristics of social media data present challenges to traditional sentiment analysis. First, social media data is often noisy, incomplete and fast-evolved which necessitates the design of a sophisticated learning model. Second, sentiment labels are hard to collect which further exacerbates the problem by not being able to discriminate sentiment polarities. Meanwhile, opportunities are also unequivocally presented. Social media contains rich sources of sentiment signals in textual terms and user interactions, which could be helpful in sentiment analysis. While there are some attempts to leverage implicit sentiment signals in positive user interactions, little attention is paid on signed social networks with both positive and negative links. The availability of signed social networks motivates us to investigate if negative links also contain useful sentiment signals. In this paper, we study a novel problem of unsupervised sentiment analysis with signed social networks. In particular, we incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model SignedSenti for unsupervised sentiment analysis. Empirical experiments on two real-world datasets corroborate its effectiveness.",
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