Abstract
Various aspects or characteristic features of an entity come into interplay to create an underlying fabric upon which sentiments blossom. In multi aspect Sentiment Analysis (SA), potentially related aspects of an entity under review are discussed in a single piece of text such as an online review. In this work, we use domain ontologies for enabling multi-aspect Sentiment Analysis. Since, domain ontologies contain the entire domain knowledge, they assist in enhanced aspect identification and detection of the latent or hidden aspects in a review document. We illustrate our approach by developing a system named Ontology driven Multi Aspect Sentiment Analysis (OMASA) system. We provide hotel reviews as input to this system and identify the panorama of explicitly expressed and latent aspects in a review using hotel domain ontology. After detecting the aspects, we link them with the corresponding opinions to gauge the sentiment pertaining to the aspects extracted. OMASA first computes sentiment scores for every aspect of the hotel. It then evaluates the overall sentiment score. On comparing with the baseline, the experimental results of OMASA show a marked improvement in the aspect level evaluation metrics \(\Delta_{aspect}^2\) and \(\rho_{aspect}\) after detecting the hidden aspects. This shows that OMASA has the potential to identify the latent aspects in text thereby improving the quality of SA.
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References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. In: Foundation and Trends in Information Retrieval, p. 2(1–2), 1–135 (2007)
Liu, B.: Sentiment Analysis and Subjectivity-Handbook of Natural Language Processing (2010)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP, pp. 1422–1432 (2015)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of NAACL-HLT, pp. 1480–1489 (2016)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Association for Computational Linguistics, pp. 417–424 (2002)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Gräbner, D., Zanker, M., Fliedl, G., Fuchs, M.: Classification of customer reviews based on sentiment analysis. In: Fuchs, M., Ricci, F., Cantoni, L. (eds.) Information and Communication Technologies in Tourism 2012, pp. 460–470. Springer Vienna, Vienna (2012). https://doi.org/10.1007/978-3-7091-1142-0_40
Celikyilmaz, A., Hakkani-Tur, D., Feng, J.: Probabilistic model-based sentiment analysis of Twitter messages. In: The 2010 IEEE International Conference on Spoken Language Technology, Workshop, pp. 79–84 (2010)
Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews. In: Proceedings of International Multi Conference on Automation, Computing, Control, Communication, and Compressed Sensing, pp. 712–717. Kerala, India (2013)
Chakraverty, S., Sharma, S., Bhalla, I.: Emotion–location mapping and analysis using twitter. J. Inf. Knowl. Manag. 14(03), 1550022 (2015)
Saraswat, M., Chakraverty, S.: Emotion distribution profile for movies recommender systems. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds.) Communication and Intelligent Systems. LNNS, vol. 204, pp. 365–373. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1089-9_30
Sharma, S., Chakraverty, S., Sharma, A., Kaur, J.: A context-based algorithm for sentiment analysis. Int. J. Comput. Vis. Robotics 7(5), 558–573 (2017)
Saraswat, M.: Efficiently finding similar objects on ontologies using earth mover’s distance. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6262, pp. 360–374. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15251-1_29
Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl. Based Syst. 37, 186–195 (2013)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Clark, K., Manning, C.D.: Deep reinforcement learning for mention-ranking Coreference Models. In: Proceedings of EMNLP (2016)
Maynard, D., Greenwood, M.A.: Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In: Lrec, 4238–4243 (2014)
Sharma, S., Chakraverty, S.: Sarcasm detection in online review text. ICTACT J. Soft Comput 08(03) (2018)
Data Mining Project focused on Twitter Sarcasm Measurement. https://github.com/dmitryvinn/twitter-sarcasm-measurement
Liebrecht, C., Kunneman, F., Van den Bosch, A.: The perfect solution for detecting sarcasm in tweets #not. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2013)
Lukin, S., Walker, M.: Really? Well. Apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue. In: The Workshop on Language Analysis in Social Media (LASM), at The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Atlanta, Georgia, USA (2013)
Chaves, M.S., Freitas, L., Vieira, R.: Hontology: a multilingual ontology for the accommodation sector in the tourism industry. In: KEOD2012- International Conference on Knowledge Engineering and Ontology Development, pp. 149–154 (2012)
Pennington, J., Socher, R., Manning, C.D.: GloVe: Global Vectors for Word Representation (2014)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. CA, USA (2013)
Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, pp. 786–794, Los Angeles (2010)
Moghaddam, S., Ester, M.: On the design of LDA models for aspect-based opinion mining. In: CIKM’12, Maui, HI, USA (2012)
Mukherjee, S., Bhattacharyya, P.: feature specific sentiment analysis for product reviews. In: CiCLing 2012
Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Miining (KDD 2010), pp. 783–792 (2010)
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Sharma, S., Saraswat, M., Dubey, A.K. (2022). Multi-aspect Sentiment Analysis Using Domain Ontologies. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., MartÃn-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_19
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