Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comment

Emotion Analysis on Turkish Texts

Automatically analyzing the user’s emotion from his/her texts has been gaining interest as a research field. Emotion classification of English texts is studied by several researchers and promising results have been achieved. In this work, an emotion classification study on Turkish texts is presented. To the best of our knowledge, this is the first study conducted on emotion classification for Turkish texts. Due to the nature of Turkish language, several pruning tasks are applied and new features are constructed in order to improve the emotion classification accuracy. We compared the performance of several classification algorithms for emotion analysis and reported the results