Open AccessSentiment Analysis in Social Media using Machine Learning Techniques
Author(s) -
Hayder A. Al-Atabi,
Ayad R. Abbas
Publication year - 2020
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2020.61.1.22
Subject(s) - sentiment analysis , computer science , social media , artificial intelligence , decision tree , process (computing) , machine learning , big data , naive bayes classifier , polarity (international relations) , data science , data mining , natural language processing , support vector machine , world wide web , genetics , biology , cell , operating system
Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.