SENTIMENT CLASSIFICATION USING THREE MACHINE LEARNING MODELS

Aimen Rmis; Muftah Alkazagli; Osamah Alloush; Salem Almadhun

doi:10.59743/jbs.v34i1.16

المؤلفون

Aimen Rmis Department of Computer Science, Faculty of Science, Alasmarya University, Libya
Muftah Alkazagli Department of Computer Science, Faculty of Science, Alasmarya University, Libya.
Osamah Alloush Department of Computer Science, Faculty of Science, Alasmarya University, Libya.
Salem Almadhun Department of Computer, Faculty of Education, Elmergib University, Libya

DOI:

https://doi.org/10.59743/jbs.v34i1.16

الكلمات المفتاحية:

Classification، JRip، J48 trees، Machine Learning، Naive Bayes، Sentiment analysis

الملخص

Feeling, emotions, views, and attitudes are all examples of sentiment. Because of the rapid growth of the World Wide Web, People frequently express their feelings via social media, blogs, ratings, and reviews on the internet. Due to the increase in textual data, it is necessary to examine the concept of expressing sentiments and calculate insights for business exploration. Sentiment analysis is frequently used by business owners and advertising agencies to develop new business strategies and advertising campaigns. This paper we examine the problem of document classification by sentiment. i.e. classify a document as negative document or as a positive document. We find out the machine learning algorithms (Naïve Bayes, rule based JRip and J48 trees based) preform quite efficiently on tackling this problem. We conclude by discussing more features that may make those algorithms perform even better than the results we report.

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