OPINION TARGET EXTRACTION WITH SENTIMENT ANALYSIS

Authors

  • Surbhi Bhatia
  • Manisha Sharma
  • Komal Kumar Bhatia
  • Pragyaditya Das

DOI:

https://doi.org/10.47839/ijc.17.3.1033

Keywords:

Opinion mining, Ontology, Information extraction, Sentiment Analysis, Supervised Learning.

Abstract

Social networks have increased their demand extensively for mining texts. Opinions are used to express views and reviews are used to provide information about how a product is perceived. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. A dynamic system is proposed based on the features using ontology followed with classification. Classifying information from such text is highly challenging. We propose a novel method of extracting aspects using ontology and further categorizing these sentiments into positive, negative and neutral category using supervised leaning technique. Opinion Mining is a natural language processing task that mine information from various text forums and classify them on the basis of their polarity as positive, negative or neutral. In this paper, we demonstrate machine learning algorithms using WEKA tool and efficiency is evaluated using information retrieval search strategies.

References

S. Bhatia, M. Sharma, K. K. Bhatia, “Strategies for mining opinions: A survey,” Proceedings of the IEEE 2nd International Conference on Computing for Sustainable Global Development (IN-DIACom), 2015, pp. 262-266.

B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1, pp. 1-167, May 2012.

B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2002, pp. 79–86.

P.D. Turney, “Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews,” Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 2002.

B. Wang, M. Liu, Deep Learning for Aspect Based Sentiment Analysis, Stanford University report, https://cs224d.stanford.edu/report/WangBo.pdf, 2015.

M. Hu, B. Liu, “Mining and summarizing customer reviews,” Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’2004), 2004, pp. 168–177.

M. Hu, B. Liu, “Mining opinion features in customer reviews,” Proceedings of the National Conference on Artificial Intelligence AAAI’04, San Jose, California, July 25-29, 2004, pp. 755–760.

N. Jindal, B. Liu, “Mining comparative sentences and relations,” Association for the Advancement of Artificial Intelligence AAAI, Vol. 22, pp. 1331-1336, 2006.

B. Liu, “Sentiment analysis and subjectivity,” Handbook of Natural Language Processing 2, 627-666, 2010.

K. Khan, B. Baharudin, A. Khan, A. Ullah, “Mining opinion components from unstructured reviews: A review,” Journal of King Saud University – Computer and Information Sciences, Vol. 26, pp. 258–275, 2014.

R. Studer, V. R. Benjamins, D. Fensel, “Knowledge engineering: principles and methods,” Data & Knowledge Engineering, Vol. 25, Issue 1, pp. 161-197, 1998.

S. R. Garner, “Weka: The waikato environment for knowledge analysis,” Proceedings of the New Zealand Computer Science Research Students Conference, 1995, pp. 57-64.

L. Farek, Identification d’opinions dans les textes arabes en utilisant les ontologies, PhD Thesis, Université Badji Mokhtar – Annaba, Algeria, 2014. [in French].

L. Zeng, F. Li, “A classification-based approach for implicit feature identification,” Proceedings of the Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, 2013, pp. 190-202.

L. Farek, T. G. Yamina, “Mining explicit and implicit opinions from reviews,” International Journal of Data Mining, Modelling and Management, Vol. 8, Issue 1, pp. 75-92, 2016.

L. A. Freitas, R. Vieira, “Ontology based feature level opinion mining for Portuguese reviews,” Proceedings of the ACM 22nd International Conference on World Wide Web, 2013, pp. 367-370.

K. Bafna, D. Toshniwal, “Feature based summarization of customers’ reviews of online products,” Procedia Computer Science, Vol. 22, pp. 142-151, 2013.

B. Bhatt, P. Bhattacharyya, “Domain specific ontology extractor for Indian languages,” Proceedings of the 10th Workshop on Asian Language Resources, COLING, Mumbai, 2012, pp. 75-84.

V. Narayanan, I. Arora, A. Bhatia, “Fast and accurate sentiment classification using an enhanced Naive Bayes model,” Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, 2013, pp. 194-201.

S.O. Orimaye, S.M. Alhashmi, E-G. Siew, “Performance and trends in recent opinion retrieval techniques,” Knowledge Engineering Review, Vol. 30, No. 1, pp. 76–105, 2015.

A.K. Samha, Y. Li, J. Zhang, “Aspect-based opinion extraction from customer reviews,” arXiv preprint arXiv:1404.1982, 2014.

K. Schoutenm, F. Frasincar, “Finding implicit features in consumer reviews for sentiment analysis,” Proceedings of the International Conference on Web engineering ICWE’2014, 2014, pp. 130–144.

T. A. Rana, Y. N. Cheah, “Aspect extraction in sentiment analysis: comparative analysis and survey,” Artificial Intelligence Review, pp. 1-25, 2016.

H. Nakagawa, T. Mori, “A simple but powerful automatic term extraction method,” Proceedings of the International Workshop on Computational Terminology, Morristown, NJ, USA, 2002.

M. Hu, B. Liu, “Mining opinion features in customer reviews,” Proceedings of the National Conference on Artificial Intelligence AAAI’04, San Jose, California, July 25-29, 2004, pp. 755–760.

S. Poria, E. Cambria, L. W. Ku, C. Gui, A. Gelbukh, “A rule-based approach to aspect extraction from product reviews,” Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP), 2014, pp. 28-37.

S. Mukherjee, J. Ajmera, S. Joshi, “Unsupervised approach for shallow domain ontology construction from corpus,” Proceedings of the 23rd ACM International Conference on World Wide Web, 2014, pp. 349-350.

http://sentiwordnet.isti.cnr.it/].

J. Kreutzer, N. Witte, Opinion Mining Using SentiWordNet, Uppsala University, 2013.

M. Al-Maimani, N. Salim, A. M. Al-Naamany, “Enhancing opinion mining classification and scoring,” Science International, Vol. 27, No. 2, 2015.

S. Bhatia, M. Sharma, K. K. Bhatia, “A novel approach for crawling the opinions from world wide web,” International Journal of Information Retrieval Research (IJIRR), Vol. 6, No. 2, pp. 1-23, 2016.

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa, “Natural language processing (almost) from scratch,” Journal of Machine Learning Research, No. 12, pp. 2493-2537, 2011.

A. Pak, P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” LREc, Vol. 10, pp. 1320-1326, 2010.

V. Umadevi, “Sentiment analysis using Weka,” International Journal of Engineering Trends and Technology (IJETT), No. 18, 2014.

Downloads

Published

2018-09-30

How to Cite

Bhatia, S., Sharma, M., Bhatia, K. K., & Das, P. (2018). OPINION TARGET EXTRACTION WITH SENTIMENT ANALYSIS. International Journal of Computing, 17(3), 136-142. https://doi.org/10.47839/ijc.17.3.1033

Issue

Section

Articles