Abstract
Sentiment analysis of text has tremendous value in many fields. But verb expression is absent, while lots of researchers concentrate on identifying opinions from adjective, adverb, and noun expressions in recent years. In this paper, we find that verb expressions in a sentence can be more important because verb expressions not only imply opinions but also give a direct way for enterprise to improve their products. It is meaningful that the verb expressions are extracted and analyzed. In order to deal with this problem, we propose a new method of linear regression optimized by particle swarm optimization to analyze verb expression extracted from reviews. Since our training data is obtained from titles of reviews whose labels are automatically inferred from review ratings, our method is able to work without manual involvement. Experimental results demonstrate our approach has great performance in terms of both precision and efficiency.
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References
Beaudouin, Valérie. Note de lecture: Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool publishers, 2012. 2012.
Chen, Zhiyuan, A. Mukherjee, and B. Liu. “Aspect Extraction with Automated Prior Knowledge Learning.” ACL 2014.
Subrahmanian, V. S., and D. Reforgiato. “AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis.” IEEE Intelligent Systems 23.4(2008):43–50.
Chunping, Ouyang, et al. “Features-level Sentiment Analysis of Movie reviews.” (2015).
Mikolov, Tomas, et al. “Neural network based language models for highly inflective languages.” IEEE International Conference on Acoustics, Speech and Signal Processing IEEE, 2009:4725–4728.
Bollegala, Danushka, D. Weir, and J. Carroll. “Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus.” IEEE Transactions on Knowledge & Data Engineering 25.8(2013):1719–1731.
Karamibekr, Mostafa, and A. A. Ghorbani. “Verb Oriented Sentiment Classification.” Ieee/wic/acm International Joint Conferences on Web Intelligence and Intelligent Agent Technology IEEE Computer Society, 2012:327–331.
Li, Huayi, et al. “Extracting Verb Expressions Implying Negative Opinions.” AAAI 2015.
Pennington, Jeffrey, R. Socher, and C. Manning. “Glove: Global Vectors for Word Representation.” Conference on Empirical Methods in Natural Language Processing 2014:1532–1543.
Mikolov, Tomas, et al. “Efficient Estimation of Word Representations in Vector Space.” Computer Science (2013).
Mikolov, Tomas, et al. “Distributed Representations of Words and Phrases and their Compositionality.” Advances in Neural Information Processing Systems 26(2013):3111–3119.
Kübler, Sandra, R. Mcdonald, and J. Nivre. “Synthesis lectures on human language technologies.” (2009).
Turney, Peter D. “Thumbs up or thumbs down: semantic orientation applied to unsupervised classification of reviews.” Meeting on Association for Computational Linguistics Association for Computational Linguistics, 2002:417–424.
Taboada, Maite, et al. “Lexicon-based methods for sentiment analysis.” Computational Linguistics 37.2(2011):267–307.
Goldberg, Yoav, and O. Levy. “word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method.” Eprint Arxiv (2014).
Eberhart, R C. A modified particle swarm optimizer. Advances in Natural Computation. Springer Berlin Heidelberg, 1998:439–439.
Kavitha, S, S. Varuna, and R. Ramya. “A comparative analysis on linear regression and support vector regression.” Online International Conference on Green Engineering and Technologies IEEE, 2017.
Acknowledgements
This work is supported in part by 2017 Central Universities Fundamental Research Project (General Program ID:56),in part by the Guangdong Province Natural Science Foundation under Grant No. 2015A030310194, in part by the Guangdong Province Science and Technology Project under Grant No. 2015A080804019 and No. 2015B010131003, in part by the Guangzhou Municipal Science and Technology Project under Grant No. 2015SX017 and No. 2015SX022, in part by the Guangdong Province Colleges Major Projects under Grant No. 2014KTSCX194, in part by the surface project of Wuxi health and family planning No. MS201716, in part by the Wuxi Science and Technology Development Guiding Project No. CSZ0N1617, and in part by 2016 NSFC-Guangdong Joint Fund of Scientific: Supercomputing Application Research Projects.
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Jiang, D., Tao, Q., Wang, Z., Dong, L. (2019). An Intelligent Logistic Regression Approach for Verb Expression’s Sentiment Analysis. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_21
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DOI: https://doi.org/10.1007/978-981-10-8944-2_21
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