Product Review Sentiment Analysis by Artificial Neural Network Algorithm

Tri Astuti, Irnawati Pratika

Abstract


Buying and selling and marketing goods and services are now done online. The online store provides facilities that enable its customers to provide review related products offered. The number of reviews received by the store, online sometimes does not allow the store online to analyze one by one. Thus, it takes the help of machines to assist in the analysis of such sentiments. Analysis of the sentiments of the review the product is done to help the shop get a general overview related to the level of consumer satisfaction. In this study, the ANN algorithm will be used to analyze sentiment for review. A product ANN algorithm used because it can provide high accuracy performance. This research resulted in a reasonably high accuracy performance is 88.2%.


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Keywords


Sentiment analysis; ANN algorithm; Product review

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References


B. Duncan, and Y. Zhang, Neural networks for sentiment analysis on twitter. In Cognitive Informatics & Cognitive Computing (ICCI* CC), 2015 IEEE 14th International Conference on (pp. 275-278). IEEE, 2015.

Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages:Feature selection for opinion classification in web forums.ACM Transactions on Information Systems, 26(3), 134. 12.

Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of twitter data. InProceeding of ACL HLT conference(pp. 3038).

Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: a case study. InProceeding of the intl. conference on recent advances in natural language processing. Borovets, BG.

Bakshy, E., Hofman, J., Mason, W., & Watts, D. (2011). Everyones an influencer:Quantifying influence on Twitter. InProceeding of ACM WSDM conference Hong Kong. China.

Barbosa, L., & Feng, J. (2010). Robust sentiment detection on twitter from biased and noisy data. InProceedings of the 23rd international conference on computational linguistics (COLING10) (pp. 3644).

Benhardus, J., & Kalita, J. (2013). Streaming trend detection in Twitter.International Journal on Web Based Communities, 9(1), 122139.

Bermingham, A., & Smeaton, A. (2010). Classifying sentiment in microblogs: Is brevity an advantage? InProceeding of ACM CIKM conference Toronto, Ontario, Canada(pp. 18331836).

Bermingham, A., & Smeaton, A. (2011). On using twitter to monitor political sentiment and predict election results. InProceeding of IJCNLP conference, Chiang Mai, Thailand.

Bifet, A., & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. In Proceeding of 13th international conference on Discovery Science Conference(pp. 115).

Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. InProceeding of the annual meetings of the association for computational linguistics(pp. 440447).

Bollen, J., Pepe, A., & Mao, H. (2011b). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the fifth international aaai conference on weblogs and social media (ICWSM 2011), July, Barcelona, Spain, (pp. 110).

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market.Journal of Computational Science, 2(1), 18.

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010). Measuring user influence in Twitter: The million follower fallacy. In Proceeding of 4th AAAI conference on weblogs and social media, Washington DC, (pp. 1017).

Chung, J., Mustafaraj, E. (2011). Can collective sentiment expressed on twitter predict political elections? InProceedings of the twenty-fifth AAAI conference on artificial intelligence(pp. 17701771).


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International Journal of Informatics and Information Systems (IJIIS)

2579-7069 (Online)
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