Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis



Every day, we deal with a lot of information on the Internet. This information can have origin from many different places such as online review sites and social networks. In the midst of this messy data, arises the opportunity to understand the subjective opinion about a text, in particular, the polarity. Sentiment Analysis and Text Classification helps to extract precious information about data and assigning a text into one or more target categories according to its content. This paper proposes a comparison between four of the most popular Text Classification Algorithms - Naive Bayes, Support Vector Machine, Decision Trees and Random Forest - based on the Amazon Unlocked mobile phone reviews dataset. Moreover, we also study the impact of some attributes (Brand and Price) on the polarity of the review. Our results demonstrate that the Support Vector Machine is the most complete algorithm of this study and achieve the highest values in all the metrics such as accuracy, precision, recal l, and F1 score.


Data Mining, Sentiment Analysis, Text Classification, Naïve Bayes, Support Vector Machine, Random Forest, Decision Trees


Data Mining


11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, August 2019


Cited by

No citations found