Comparative Study of Sentiment Analysis for Interpreting the Customer Interest in Women Fashion Clothes
DOI:
https://doi.org/10.33019/jurnalecotipe.v12i2.4561Keywords:
Comparative Study, Customer Review, E-Commerce, Machine Learning, Sentiment AnalysisAbstract
Public sentiment is widely recognized as a key factor influencing fluctuations in stock prices, product sales, and emerging trends. Since the user interest analysis played an essential role in representing the trend of the market and it is also extremely useful for generating the strategy and decision of trading, analyzing the public sentiment is quite important. In the contemporary era, virtual space has been perpetually evolving and used in plenteous applications including analyzing customer interest. This research work aimed to conduct comparative study in analyzing customer interest about women fashion clothes using sentiment analysis method-based machine learning approach. The proposed study was initiated by conducting data acquisition process. It was then continued with labelling and predicting the user sentiment by comparing eleven machine learning approaches. According to comparison result, Naïve Bayes successfully obtained the best performance with accuracy of 94%, precision of 87%, recall of 82%, f1-score of 84%. It can be inferred that Naïve Bayes was viable approach for predicting the user sentiment.
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