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IJSTR >> Volume 9 - Issue 5, May 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Dependency-Directed Opinion Analytics For Product Review Classification Based On Keyphrase

[Full Text]

 

AUTHOR(S)

Dhanasekaran K, Manikandan Ramasamy, Raju Shanmugam, M Prathilothamai

 

KEYWORDS

Product review classification, opinion mining, multiclass text classification, machine learning.

 

ABSTRACT

Text classification on product reviews has long been a challenging task due to the rapid growth of Web usage that has resulted in a huge volume of unstructured data. Recently, Opinion mining has been emerged as an important discipline to process the unstructured data. Although several opinion mining approaches addressed the problem of dealing with unstructured data, further research opportunities are available due to the issues like class imbalance, and complexity in text data analytics that affects the performance of opinion learning. Further, the manual text classification consumes a lot of time while identifying useful information. Also, the existing approaches for classifying texts based on majority category are not enough for realistic scenarios specifically in large scale applications. This paper proposes a prediction approach which focuses on obtaining useful information by using keyphrase and category labels. In this paper, we first investigate existing machine learning techniques to classify customer opinions with respect to multiple categories. Moreover, we propose keyphrase based multiclass text classification that finds insights from opinions of various customers on financial products and services. The result of our experiment shows that our dependency-directed opinion learning can show significant improvement over precision, recall, and F1-measure.

 

REFERENCES

[1]. Ali W, Rito T, Reinert G, Sun F, Deane CM (2014) Alignment-free protein interaction network comparison. Bioinformatics30:i430-i437.
[2]. Jamieson DG, Gerner M, Sarafraz F, Nenadic G, Robertson DL (2012) Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database. Database2012: p.bas023.
[3]. G. Geetika, and Y.Divakar, Sentiment Analysis of Twitter Data using Machine learning approaches and semantic analysis, IEEE Conference paper, DOI: 10.1109/IC3.2014.6897213, 2014.
[4]. Y. Sunmoo, E.Noemie, and B.Suzanne, A practical approach for content mining of tweets, American Journal of Preventive Medicine, vol.45, pp.122-129, 2014.
[5]. V.Soroush, Z.Helen, R.Deb, Enhanced twitter sentiment classification using contextual information,In Proceedings of WASSA, pp.16-24, 2015.
[6]. C.Brendan, B.Ramnath, R.R, Bryan, A.S. Noah, From tweets to polls: linking text sentiment to public opinion time series,In Proceedings of the fourth international conference on Weblogs and Social Media, pp.122-129, 2010.
[7]. S.Varsha, S.Vijaya, P.Apashabi, Sentiment analysis on twitter data, International Journal of Innovative Research in Advanced Engineering, vol.2, pp.178-183, 2015.
[8]. H.Ali, M.Sana, K.Ahmad, and S.Shahaboddin, Machine learning-based sentiment analysis for twitter accounts, Mathematical and Computational Applications, vol.23, pp.1-15, 2018.
[9]. Z.Yanchang, Analysing twitter data with text mining and social network analysis, In Proceedings of AusDM, pp.41-47, 2013.
[10]. A.H. Syed Akib, M.TahmidEkram, I.MohammadSamiul, A.Faysal, and M.R. Rashedur, Localized twitter opinion mining using sentiment analysis, Decision analytics, vol.8, pp.1-19, 2015.
[11]. V.Svitlana, B.Yoram, V.D. Benjamin, Mining user interests to predict perceived psycho-demographic traits on twitter,In IEEE Second International conference on Big data computing service and applications, 2016.
[12]. S. Volkova, G. Coppersmith, and B. Van Durme, Inferring user political preferences from streaming communications, In Proceedings of ACL, pp. 186–196, 2014.
[13]. P. Kapanipathi, P. Jain, C. Venkataramani, and A. Sheth, User interests identification on twitter using a hierarchical knowledge base in The Semantic Web: Trends and Challenges, Springer, pp. 99–113, 2014.
[14]. R. Cohen and D. Ruths, Classifying political orientation on Twitter: It’s not easy!,In Proceedings of ICWSM, 2013.
[15]. H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, R. E.Lucas, M. Agrawal, G. J. Park, S. K. Lakshmikanth, S. Jha, M. E.Seligman et al., Characterizing geographic variation in well-being using tweets,In Proceedings of ICWSM, 2013.
[16]. Q.Mei, X.Ling, M.Wondra, H.Su, and C.X. Zhai, Topic sentiment mixture: modeling facets and opinions in weblogs, In Proceedings of the 16th International conference on WWW, 2007.
[17]. G. Alec, H.Lei, and B.Richa, Twitter sentiment analysis, Entropy, 17, 2009.
[18]. Gentry et. Al., Rgraphviz: Provides plotting capabilities for R graph objects, R package version 2.4.1, 2013.
[19]. B.Luciano, and F.Junlan, Robust sentiment detection on twitter from biased and noisy data,In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 36-44, 2010.
[20]. G.Vinodhini, R. M. Chandrasekaran, Sentiment Analysis and Opinion Mining: A Survey, IEEE, vol.2, 2012.
[21]. Theodosiou T, Darzentas N, Angelis L, Ouzounis CA (2008) PuReD-MCL: a graph-based PubMed document clustering methodology. Bioinformatics24:1935-1941.
[22]. Hettne KM, Stierum RH, Schuemie MJ, Hendriksen PJ, Schijvenaars BJ, Van Mulligen EM, Kleinjans J, Kors JA (2009) A dictionary to identify small molecules and drugs in free text. Bioinformatics25:2983-2991.
[23]. Dahlmeier D, Ng HT (2010) Domain adaptation for semantic role labeling in the biomedical domain. Bioinformatics26:1098-1104.
[24]. Leaman R, Doğan RI, Lu Z (2013) DNorm: disease name normalization with pairwise learning to rank. Bioinformatics p.btt474.
[25]. Wiegers TC, Davis AP, Mattingly CJ (2014) Web services-based text-mining demonstrates broad impacts for interoperability and process simplification. Database,2014: p.bau050.
[26]. Krämer A, Green J, Pollard J, Tugendreich S (2013) Causal analysis approaches in ingenuity pathway analysis (ipa). Bioinformatics p.btt703.
[27]. Oesper L, Satas G, Raphael BJ (2014) Quantifyingtumor heterogeneity in whole-genome and whole-exome sequencing data. Bioinformatics30:3532-3540.
[28]. Aßhauer KP, Wemheuer B, Daniel R, Meinicke P (2015) Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics31:2882-2884.
[29]. Sætre R, Yoshida K, Miwa M, Matsuzaki T, Kano Y, Tsujii JI (2010) Extracting protein interactions from text with the unified AkaneRE event extraction system. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)7:442-453.
[30]. Naveed H, Hameed US, Harrus D, Bourguet W, Arold ST, Gao X (2015) An integrated structure-and system-based framework to identify new targets of metabolites and known drugs. Bioinformatics p.btv477.
[31]. Bogdanov P, Singh AK (2010) Molecular function prediction using neighborhood features. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)7:208-217.
[32]. Paul TK, Iba H (2009) Prediction of cancer class with majority voting genetic programming classifier using gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 6:353-367.