TITLE:
Analysing Effectiveness of Sentiments in Social Media Data Using Machine Learning Techniques
AUTHORS:
Thambusamy Velmurugan, Mohandas Archana, Ajith Singh Nongmaithem
KEYWORDS:
Support Vector Machine, Random Forest Algorithm, Naive Bayes Algorithm, Machine Learning Techniques, Decision Tree Algorithm
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.1,
January
31,
2025
ABSTRACT: Every second, a large volume of useful data is created in social media about the various kind of online purchases and in another forms of reviews. Particularly, purchased products review data is enormously growing in different database repositories every day. Most of the review data are useful to new customers for theier further purchases as well as existing companies to view customers feedback about various products. Data Mining and Machine Leaning techniques are familiar to analyse such kind of data to visualise and know the potential use of the purchased items through online. The customers are making quality of products through their sentiments about the purchased items from different online companies. In this research work, it is analysed sentiments of Headphone review data, which is collected from online repositories. For the analysis of Headphone review data, some of the Machine Learning techniques like Support Vector Machines, Naive Bayes, Decision Trees and Random Forest Algorithms and a Hybrid method are applied to find the quality via the customers’ sentiments. The accuracy and performance of the taken algorithms are also analysed based on the three types of sentiments such as positive, negative and neutral.