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- Publisher Website: 10.1109/ACIIAsia.2018.8470357
- Scopus: eid_2-s2.0-85055531964
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Conference Paper: Topic-Adaptive Sentiment Lexicon Construction
Title | Topic-Adaptive Sentiment Lexicon Construction |
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Authors | |
Keywords | Sentiment Lexicon Sentiment Analysis Opinion mining |
Issue Date | 2018 |
Citation | 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018, 2018, article no. 8470357 How to Cite? |
Abstract | © 2018 IEEE. In this paper, we propose a novel sentiment-aware topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level sentiment classification tasks. It's widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. In this paper, we address this issue by assigning multiple pairs of topics and sentiments for each word. In TaSL, documents are represented by multiple pairs of topics and sentiments and words are characterized by a multinomial distribution over the pairs of topics and sentiments. The main advantage of TaSL is that the sentiment polarities of words in different topics can be sufficiently captured. This model is beneficial to construct a topic-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets RM, OMD, semEval13A and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results show that TaSL performs better than the state-of-the-art methods. |
Persistent Identifier | http://hdl.handle.net/10722/276611 |
DC Field | Value | Language |
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dc.contributor.author | Deng, Dong | - |
dc.contributor.author | Jing, Liping | - |
dc.contributor.author | Yu, Jian | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:34:08Z | - |
dc.date.available | 2019-09-18T08:34:08Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018, 2018, article no. 8470357 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276611 | - |
dc.description.abstract | © 2018 IEEE. In this paper, we propose a novel sentiment-aware topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level sentiment classification tasks. It's widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. In this paper, we address this issue by assigning multiple pairs of topics and sentiments for each word. In TaSL, documents are represented by multiple pairs of topics and sentiments and words are characterized by a multinomial distribution over the pairs of topics and sentiments. The main advantage of TaSL is that the sentiment polarities of words in different topics can be sufficiently captured. This model is beneficial to construct a topic-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets RM, OMD, semEval13A and semEval16B were presented to demonstrate the usefulness of the proposed approach. The results show that TaSL performs better than the state-of-the-art methods. | - |
dc.language | eng | - |
dc.relation.ispartof | 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018 | - |
dc.subject | Sentiment Lexicon | - |
dc.subject | Sentiment Analysis | - |
dc.subject | Opinion mining | - |
dc.title | Topic-Adaptive Sentiment Lexicon Construction | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ACIIAsia.2018.8470357 | - |
dc.identifier.scopus | eid_2-s2.0-85055531964 | - |
dc.identifier.spage | article no. 8470357 | - |
dc.identifier.epage | article no. 8470357 | - |