Opinion Mining with Real Time Ontology Streaming Data
DOI:
https://doi.org/10.61841/k5406z57Keywords:
Sentiment Analysis, Semantic Web, Data Mining, Prediction, Ontology.Abstract
Social networking, the fastest mode of finding individuals with heterogeneous opinions on various issues, is the current trend in today’s world. There are many social networking sites like Facebook, Twitter, etc. where not only information exchange but also sharing opinions happens. Sentiment analysis, or opinion mining, deals with various emotions and its analysis as positive, negative, and neutral due to the mood of a particular individual. The work ultimately focuses on a system built with opinions mined from data extracted live from Twitter. The development in a particular field could be efficiently analyzed based on opinion mining. Extraction of major important features is done with ontologies and its analysis with feature quotient. In the proposed work, all attributes are analyzed and individual scores are allotted.
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References
[1] AbinashTripathy, Ankit Agarwal, and Santanu Kumar Rath (2015). Classification of Sentimental reviews using
Machine Learning Techniques. Procedia Computer Science, Vol.57, pp. 821-829.
[2] Hassan Saif, Yulan He and Harith Alani. (2012).Semantics sentiment analysis of twitter, The Semantic Web
ISWC 2012, Vol. 7469, Lecture Notes in Computer Science, pp. 508-524.
[3] Bo Pang and Lillian Lee (2004).A Sentimental Education: Sentiment Analysis Using Subjectivity
Summarization Based on Minimum Cuts. ACL '04 Proceedings of the 42nd Annual Meeting on Association for
Computational Linguistics, Article No. 271.
[4] Akish Kumar and Teeja Mary Sebastian (2012), Sentiment Analysis on Twitter. IJCSI International Journal of
Computer Science Issues, Vol. 9, Issue 4, No. 3.
[5] Efstratios Kontopoulos, Christos Berberdis, Theologos Dergiades (2013).Ontology-based Sentiment
Analysis of Twitter Posts. Expert Systems with applications, Vol. 40(10), pp. 4065–4074.
[6] Ruby Prabowo, Mike Thelwall. (2009). Sentiment Analysis: A combined approach. Journal of Informetrics,
Vol. 3 (2), pp. 143-157.
[7] Isidro penalver-Martinez, Francisco Garcia-Sanchez, Rafael Valencia-Gracia, Miguel Angel Rodriguez-Garcia,
Valentin Moreno, Anabel Fraga, and Jose Luis Sanchez-Cervantes. (2014). Feature-based opinion mining through
ontologies. Expert Systems with Applications, Vol. 41(13), pp. 5995–6008.
[8] Efthymioskouloumpis, Theresa Wilson, Johanna Moore. (2011) Twitter Sentiment Analysis: The Good and the Bad
and the OMG, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.
[9] Jantimaponpinij and Adhithya.k.ghose (2008).Ontology-based classification methodology for online consumer
reviews. WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence
and Intelligent Agent Technology, Vol. 01, pp. 524.
[10] Lili Zhao and Chunoing Li (2014). Ontology-based opinion mining for movie reviews, KSEM’09 Proceedings
of the 3rd International Conference on Knowledge Science, Engineering, and Management, pp. 204-214.
[11] Walaamedhat, Ahamehassan, and Hodakorashy (2014). Sentiment analysis algorithms and applications: A survey.
Ain Shams Engineering Journal, Vol. 5(4), pp. 1093–1113.
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