Opinion Mining for Laptop Reviews using Naïve Bayes
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Abstract
This research is to develop an opinion mining application which allows users to clarify what the reviews on the laptop mentioned. The aim of the research is to analyze user’s opinions from laptop reviews on popular online communities. The proposed methodology is composed of four essential processes: preparing data for analysis, detecting subjective text paragraphs, identifying the aspects and classifying the sentiments of text paragraphs. The subjective textual contents are determined by detecting subjective words occurred in the sentences of text paragraphs. Then, only the subjective paragraphs might be classified into specific aspects using comparisons with the vocabularies of aspect domains. Finally, the paragraph sentiments will be categorized into positive or negative opinions using the Naïve Bayes classifier. The experimental results with the performance evaluation showed that the accuracy and precision of the subjective detection of text paragraphs are greater than 90%. In addition, the accuracy and precision of sentiment classification are more than 70%. Therefore, this tool can help consumers in categorizing laptop review paragraphs into aspects and sentiment groups for making selections before purchasing laptops.
Keywords: opinion mining; review analysis; laptop reviews; Naïve Bayes
*Corresponding author: Tel..: +66 22 18 5170 Fax: +66 22 55 2287
E-mail: pakawan.p@chula.ac.th
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References
[2] Chatchaithanawat, T. and Pugsee, P., 2015. A framework for laptop review analysis. Proceeding of the 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications, Chonburi, Thailand, August 19-22, 2015, 1-5.
[3] B. Pang, B., Lee, L. and Vaithyanathan, S., 2002. Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Philadelphia Pennsylvania, USA, July 6-7, 2002, 79-86.
[4] Turney, P.D., 2002. 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, July 6-12, 2002, 417-424.
[5] Govindaraj, S. and Gopalakrishnan, K., 2016. Intensified sentiment analysis of customer product reviews using acoustic and textual features. ETRI Journal, 38(3), 494-501.
[6] Multi-perspective Question Answering, University of Pittsburgh, 2005. The MPQA Opinion Corpus. [online] Available at: http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/
[7] Text Learning Group, 2010. The SentiWordNet SentimentLlexicon. [online] Available at: http://sentiwordnet.isti.cnr.it
[8] Baccianella, S., Esuli, A. and Sebastian F., 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the 7th International Conference on Language Resources and Evaluation, Valletta, Malta, May 19-21, 2010, 2200-2204.
[9] Mitchell, T., 1997. Machine Learning, USA: McGraw-Hill Education -Europe.
[10] Valdivia, A., Luzon, M. V. and Herrera, F., 2017. Sentiment analysis in TripAdvisor. IEEE Intelligent System, 32(4), 72-77.
[11] Pugsee, P., Nussiri, V. and Kittirungruang, W., 2019. Opinion mining for skin care products on Twitter. Communications in Computer and Information Science, 937, 261-271.
[12] Pugsee, P., Sombatsri, P. and Juntiwakul, R., 2017. Satisfactory analysis for cosmetic product review comments. Proceeding of 2017 International Conference on Data Mining, Communications and Information Technology, Phuket, Thailand, May 25-27, 2017, 1-6.
[13] Stanford Natural Language Processing Group, Stanford University, 2008. Basic English Stanford Tagger. [online] Available at: https://nlp.stanford.edu/software/tagger.shtml
[14] Massachusetts Institute of Technology and Finlayson, M.A., 2013. WordnetStemmer. [online] Available at: https://projects.csail.mit.edu/jwi/api/edu/mit/jwi/morph/WordnetStemmer.html
[15] Yamamoto, Y., Kumamoto, T. and Nadamoto, A., 2014. Role of emoticons for multidimensional sentiment analysis of twitter. Proceeding of the 16th International Conference on Information Integration and Web-based Applications & Services, Hanoi, Viet Nam, December 4-6, 2014, 107-115.
[16] Anthony, L., 2019. AntConc: A Freeware Corpus Analysis Toolkit for Concordance and Text Analysis. [online] Available at: https://www.laurenceanthony.net/software
[17] Machine Learning Group, University of Waikato, 2019. Weka: The Workbench for Machine Learning. [online] Available at: https://www.cs.waikato.ac.nz/ml/weka/
