SEMANTIC GRAPH BASED TERM EXPANSION FOR SENTENCE-LEVEL SENTIMENT ANALYSIS
DOI:
https://doi.org/10.47839/ijc.19.4.2000Keywords:
Semantic graph, Sentiment analysis, POS-based Term expansion, Machine learning, Term Prior PolarityAbstract
The semantic orientation (also referred to as prior polarity) of a word plays an important role in automatic sentence-level sentiment analysis. Several approaches have been proposed wherein a lexicon of words marked with their polarities is exploited to infer the meaning of sentences. However, relying on prior word polarity may produce inaccurate decisions. This is because we may find negative-sentence sentiments that include words with positive prior polarities or vice versa. In this article, we propose an approach to sentence-level sentiment analysis that exploits knowledge encoded in heavy-weight semantic graphs to assist in discovering the meaning of a word in the context of the sentence where it appears. In this context, we build contextual semantic networks for indexing sentences and expand them with semantically/lexically-relevant terms in an attempt to disambiguate the meanings of word mentions in sentences. In order to verify the effectiveness of the proposed approach, we have developed a prototype system using a real-world dataset that contains 46830 sentiment sentences along with a gold-standard that comprises 10000 movie reviews that are labelled under five sentiment categories (very negative, negative, neutral, positive, very positive). Findings indicate that enriching the semantic graphs of sentiment sentences with NOUN-based synonyms and hypernyms has improved the overall quality of baseline sentiment analysis techniques.
References
S. Bhatia, M. Sharma, K. K. Bhatia, and P. Das, “Opinion target extraction with sentiment analysis,” International Journal of Computing, vol. 17, no. 3, pp. 136-142, 2018.
V. Kharde and S. S. Sonawane, “Sentiment analysis of twitter data: a survey of techniques,” International Journal of Computer Applications, vol. 139, no. 11, pp. 5-15, 2016.
C.-H. Du, M.-F. Tsai, and C.-J. Wang, “Beyond word-level to sentence-level sentiment analysis for financial reports,” Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 1562-1566: IEEE.
H. J. VL and S. Duraisamy, “Novel pre-processing framework to improve classification accuracy in opinion mining,” International Journal of Computing, vol. 17, no. 4, pp. 234-242, 2018.
T. Cu, H. Schneider, and J. V. Scotter, “How does sentiment content of product reviews make diffusion different?,” Journal of Computer Information Systems, vol. 59, no. 2, pp. 127-135, 2019.
M. Poturak, S. Softic, “Influence of social media content on consumer purchase intention: Mediation effect of brand equity,” Eurasian Journal of Business and Economics, vol. 12, no. 23, pp. 17-43, 2019.
B. Agarwal, N. Mittal, P. Bansal, S. Garg, “Sentiment analysis using common-sense and context information,” vol. 2015, Article ID 715730, p. 30, 2015. https://doi.org/10.1155/2015/715730
K. Schouten, F. Frasincar, and F. de Jong, “Ontology-enhanced aspect-based sentiment analysis,” Proceedings of the International Conference on Web Engineering, 2017, pp. 302-320.
N. Zainuddin, A. Selamat, and R. J. Ibrahim, “Hybrid sentiment classification on twitter aspect-based sentiment analysis,” Applied Intelligence, vol. 48, no. 5, pp. 1218-1232, 2018.
M. Horakova, “Sentiment analysis tool using machine learning,” Global Journal on Technology, vol. 2015, no. 5, pp. 195-204, 2015.
B. Jeong, J. Yoon, and J.-M. J. I. J. o. I. M. Lee, “Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis,” International Journal of Information Management, vol. 48, pp. 280-290, 2019.
N. K. Singh, D. S. Tomar, and A. K. Sangaiah, “Sentiment analysis: a review and comparative analysis over social media,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 1, pp. 97-117, 2020.
B. Liu, “Sentiment analysis and subjectivity,” Handbook of Natural Language Processing, vol. 2, no. 2010, pp. 627-666, 2010.
B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1-135, 2008. https://doi.org/10.1561/1500000011
B. Liu and L. Zhang, “A survey of opinion mining and sentiment analysis,” Mining Text Data: Springer, 2012, pp. 415-463. https://doi.org/10.1007/978-1-4614-3223-4_13
B. S. Rintyarna, R. Sarno, C. Fatichah, “Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context,” International Journal of Information and Decision Sciences, vol. 12, no. 1, pp. 75-101, 2020. https://doi.org/10.1504/IJIDS.2020.104992
A. Sharma and S. Dey, “A document-level sentiment analysis approach using artificial neural network and sentiment lexicons,” ACM SIGAPP Applied Computing Review, vol. 12, no. 4, pp. 67-75, 2012.
D. Bespalov, B. Bai, Y. Qi, and A. Shokoufandeh, “Sentiment classification based on supervised latent n-gram analysis,” Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 2011, pp. 375-382.
E. Kouloumpis, T. Wilson, and J. Moore, “Twitter sentiment analysis: The good the bad and the omg!,” Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 2011, pp. 538-541.
H. Saif, Y. He, and H. Alani, “Semantic sentiment analysis of twitter,” Proceedings of the International Semantic Web Conference, 2012, pp. 508-524.
L. Barbosa and J. Feng, “Robust sentiment detection on twitter from biased and noisy data,” Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 36-44.
A. Bifet and E. Frank, “Sentiment knowledge discovery in twitter streaming data,” Proceedings of the International Conference on Discovery Science, 2010, pp. 1-15.
A. Pak and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," i Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), 2010, http://www.lrec-conf.org/proceedings/lrec2010/pdf/385_Paper.pdf.
H. Saif, Y. He, M. Fernandez, and H. Alani, “Semantic patterns for sentiment analysis of Twitter,” Proceedings of the International Semantic Web Conference, 2014, pp. 324-340.
W. Zhao et al., "Weakly-supervised deep embedding for product review sentiment analysis," IEEE Transactions on Knowledge Data Engineering, vol. 30, no. 1, pp. 185-197, 2017.
S. Xiong, H. Lv, W. Zhao, and D. Ji, “Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings,” Neurocomputing, vol. 275, pp. 2459-2466, 2018.
M. Hu and B. Liu, “Mining and summarizing customer reviews,” Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 168-177.
V. Gnana Kirubaharan, P. Elumalai, and A. Yovan Felix, “Online product ranking based on reviews,” ARPN Journal of Engineering and Applied Sciences, vol. 12, no. 13, pp. 4097-4100, 2006.
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