ANALISA SENTIMEN REVIEW PRODUK HANDPHONE PADA SITUS AMAZON MENGGUNAKAN PENDEKATAN LEXICON BERDASARKAN SENTIWORDNET

Anugerah Tri Siswanto, Rani Rotul Muhima, Septiyawan Rosetya Wardhana

Abstract


Sentiment analysis is part of opinion mining, is the process of understanding, extracting and processing textual data automatically to obtain information. It is done to find out the attitude of a speaker or writer later to be classified into positive or negative sentiment groups. In this study, the determination of the word value in the document is determined using Lexicon Sentiwordnet as a benchmark for word value, the review document will go through several pre-processing stages including Delete URLs, Remove Punctuation, Casefolding, Delete Stopwords, POS Tagging, Sentence tokens, and tokens. word so that the review document data are structured. Furthermore, the Sentiwordnet Interpretation calculation is carried out to determine the value of a term or word in the document to determine whether the word is a positive or negative word based on its category in the POS Tagging process. Then do the calculation of the term score summation, calculate the value of the sentence, calculate the text score, and the results of the rating system that will generate a rating to determine the quality of a mobile phone product based on user reviews.

Keywords


Sentiment Analysis; Opinioin Mining; SentiWordNet; Product revierws; Amazon; Handphone

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References


A. Bhatt, A. Patel, H. Chheda, and K. Gawande, “Amazon Review Classification and Sentiment Analysis,” Int. J. Comput. Sci. Inf. Technol., vol. 6, no. 6, pp. 5107–5110, 2015.

A. Rahman and M. S. Hossen, “Sentiment Analysis on Movie Review Data Using Machine Learning Approach,” 2019 Int. Conf. Bangla Speech Lang. Process. ICBSLP 2019, pp. 27–28, 2019.

B. Saberi and S. Saad, “Sentiment analysis or opinion mining: A review,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 5, pp. 1660–1666, 2017.

A. Cernian, V. Sgarciu, and B. Martin, “Sentiment analysis from product reviews using SentiWordNet as lexical resource,” Proc. 2015 7th Int. Conf. Electron. Comput. Artif. Intell. ECAI 2015, pp. 15–18, 2015.

R. Feldman and J. Sanger, The Text Mining Handbook. 2006.

B. Ohana and B. Tierney, “Sentiment classification of reviews using SentiWordNet,” 9th. IT T Conf., no. January 2009, 2009.

C. C. Aggarwal and C. X. Zhai, “Mining text data,” Min. Text Data, vol. 9781461432234, pp. 1–522, 2013.

S. Wei and C. P. Soon, “Genetic algorithm-based text clustering technique,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4221 LNCS, pp. 779–782, 2006.

A. Hamouda and M. Rohaim, “Reviews classification using sentiwordnet lexicon,” World Congr. Comput. Sci. …, vol. 2, no. 2, pp. 120–123, 2011.




DOI: https://doi.org/10.31284/j.kernel.2022.v3i1.1928

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