Open Access Open Access  Restricted Access Subscription or Fee Access

A Peer Review of different techniques of Sentiment analysis and Methodology

Tanya Shruti, Lal Babu Purbey

Abstract


ABSTRACT

This paper provides an overview of various approaches that are mainly used for sentiment analysis. It focused on the description of the latest technology and tools introduced for this purpose. There are various algorithms thatare used for opinion mining. Sentiment analysis is used to extract opinionated information from various sources by applying different techniques such as Natural language processing technique (NLP), Computational Linguistics and Text Analysis. In this paper, we are going to discuss the different levels of sentiment analysis and different approaches for sentiment classification. In this paper, we discussed the different levels of sentiment and modern approach to analyze this.

 

Keywords: Sentiment analysis, Machine learning, opinion extraction, sentiment analysis tools, feature level analysis


Full Text:

PDF

References


REFERENCES

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

P.Turney, 2002, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceeding of 40th annual meeting of the Association for Computational Linguistics (ACL), pp. 417-424.

E. Riloff, and J. Wiebe, 2003, “Learning Extraction Patterns for Subjective Expressions”, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Japan, Sapporo.

H.Yu, and V.Hatzivassiloglou, 2003, “Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences”, published in ACM digital library EMNLP-03, conference on empirical methods in natural language processing.

T. Wilson, J. Wiebe, and R. Hwa, 2004, “Just how mad are you? Finding strong and weak opinion clauses”, In the Association for the Advancement of Artificial Intelligence, pp. 761-769.

B. Liu, and J. Cheng, 2005, “Opinion Observer: Analyzing and comparing opinions on the web”, Proceedings of WWW.

A.M.Popescu, O. Etzioni, 2005, “Extracting Product Features and Opinions from Reviews”, In Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, pp.339–346

Christopher Scaffidi, Kevin Bierhoff, Eric Chang, MikhaelFelker, Herman Ng and Chun Jin, 2007, “Red Opal: product-feature scoring from reviews”, Proceedings of 8th ACM Conference on Electronic Commerce, pp. 182191, NewYork.

X. Ding, B. Liu, and P. S. Yu, 2008, “A holistic lexicon-based approach to opinion mining”, ACM Proceedings of the Conference on Web Search and Web Data Mining (WSDM).

K. Denecke, 2008, “Using SentiWordNet for Multilingual Sentiment Analysis”, in Proceedings of the International Conference on Data Engineering, Workshop on Data Engineering for Blogs, Social Media, and Web 2.0, Cancun

B. Liu, 2008, “Opinion Mining and Summarization”, World Wide Web Conference, Beijing, China.

Bo Pang and Lillian Lee, 2008, “Opinion mining and sentiment analysis”, in Foundations and Trends in Information Retrieval Vol.

B. Liu., 2010, “Sentiment Analysis: A Multifaceted Problem”, Invited paper, IEEE Intelligent Systems.

B. Liu., 2010, “Sentiment Analysis and Subjectivity Second Edition”, the Handbook of Natural Language Processing.

B. Liu, 2010, “Opinion Mining and Sentiment Analysis”, NLP Meets Social Sciences”, STSC, Hawaii.

B. Liu, 2011, “Opinion Mining and Sentiment Analysis”, AAAI, San Francisco, USA.

S.Agrawal and T.J.Siddiqui, 2012 “Featurebased Star Rating of Reviews: A Knowledge-Based Approach for Document Sentiment Classification” in International Journal of Hybrid Information Technology Vol. 5.

B.LIU, 2012, “Sentiment Analysis and Opinion mining”, Bing Liu [email protected].

Emma Haddia, XiaohuiLiua, Yong Shib. 2013. “The Role of Text Pre-processing in Sentiment Analysis”, in Procedia Computer Science 17 (2013) 26 – 32.

GautamiTripathi and Naganna S. 2014. “Opinion Mining: A Review,” in International Journal of Information & Computation Technology.ISSN 0974-2239 Volume 4.

Muhammad ZubairAsghar, Aurangzeb Khan, Shakeel Ahmad, FazalMasudKundi. 2014. “A Review of Feature Extraction in Sentiment Analysis,” in Journal of Basic and Applied Scientific Research ISSN 2090-4304.

S. Kasthuri, Dr. L. Jayasimman, Dr. A. NishaJebaseeli. 2016. “An Opinion Mining and Sentiment Analysis Techniques: A Survey,” in International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02.

Supriya B. Moralwar, Sachin N. Deshmukh 2015. “different approaches of SentimentAnalysis” International

Journal of Computer Sciences and Engineering Vol.-3(3), PP(160-165) Mar 2015, E-ISSN: 2347-2693

SweetySinghal, SaurabhMaheswari, Monalisa Meena 2020. “Bagged Random Forest Approach to Classify

Sentiments Based on Technical Words” Recent Trends in Communication and Intelligent Systems. Springer, Singapore.

Tanya Shruti, Manish Choudhary, 2016.” Text mining at Feature level: A Review”Internation Journal of Advanced Engineering, Management and Science, Vol-2 Issue-9.

Tanya Shruti and Manish Choudhary. "Feature Based Opinion Mining on Movie Review." International Journal of Advanced Engineering Research and Science, vol. 3, no. 9, Sep. 2016.

Sweety Singhal, SaurabhMaheswari, Monalisa Meena “Survey of challenges in sentiment analysis”, Recent Findings in Intelligent Computing Techniques, 2018, Volume 709ISBN: 978-981-10-8632-8

Tanya Shruti and Manish Choudhary. "Feature Based Opinion Mining on Movie Review." International Journal of Advanced Engineering Research and Science, vol. 3, no. 9, Sep. 2016.

Sweety Singhal, Saurabh Maheswari, Monalisa Meena “ Survey of challenges in sentiment analysis”,Recent Findings in Intelligent Computing Techniques, 2018, Volume 709ISBN: 978-981-10-8632-8

V. Ganganwar, R.Rajalakshmi, “Implicit aspect extraction for sentimental analysis: A survey of recent approaches”, Published in Procedia Computer Science 165 (2019) 485–491,international conference on recent trends in ad vanced computing, 2019.

N. Saeed, N. Helal, N. L. Badr and T. Gharib, “ An enhanced feature based sentiment analysis approach”, Published in Wiley periodical Publication, WIREs Data Mining Knowledge and Discovery, 2019.

O. Alqaryouti, N. Azza, A. Monem, K. Shallan,” Aspect based sentiment analysis using smart government review data”, Published in Applied comput ing and informatics, 2019.

A. Yadav and D. K. Vishwakarma,” A Comparative Study on bio-inspired algorithm for sentiment analysis”, published in springer Science +Business Media, LLC, part of Springer Nature, 2020.

M. Ahmad, Q. Chen, Z. Li, “Construction domain dependent sentiment dictionary for sentiment analysis”, Published in Springer-Verlag London Ltd., part of Springer Nature, 2020.




DOI: https://doi.org/10.37628/ijocspl.v6i2.647

Refbacks

  • There are currently no refbacks.