People use online platforms such as Facebook, Twitter, etc. for social networking and share their opinions, feelings or beliefs with others. Sharing is done by posts on these platforms. Sentiment analysis or opinion mining of these posts using machine learning techniques is of great significance. The analysis is generally carried out with sentiment, subjectivity analysis or polarity calculations. In this study, we performed a sentiment analysis of text messages using supervised machine learning techniques. They are mainly online product reviews, general tweets in Tweeter and movie reviews. Messages are pre-processed and then used three different machine learning techniques, namely Naïve Bayes, Decision Tree and Support Vector Machine (SVM) for sentiment analysis.
Published In:IJCAT Journal Volume 7, Issue 6
Date of Publication : June 2020
Pages : 103-109
Figures :04
Tables :04
Abhishek Bhagat :
is currently pursuing his BTech (CSE) from the Department of CSE, School of Technology, Assam Don Bosco University. Currently, he is in his final year. His areas of interest are Machine Learning, Big data analytics and Natural Language Processing.
Akash Sharma :
is currently pursuing his BTech (CSE) from the Department of CSE, School of Technology, Assam Don Bosco University. He is a final year student at present. His areas of interest are Machine Learning, Internet of Things (IoT) and Natural Language Processing (NLP).
Dr. Sarat Kr. Chettri :
is an Assistant Professor in the Department of Computer Applications, School of Technology, Assam Don Bosco University. He has made several publications in international journals and conferences. His research area includes data science, machine learning and Internet of Things (IoT).
Machine Learning, Natural Language Processing, Sentiment Analysis, Twitter
In this paper, text data from product reviews, general tweets and movie reviews are taken into account to assess the polarity (positive or negative) of messages or tweets. We used the classification algorithms namely SVM, Naïve
Bayes and decision tree. We evaluated our models on the basis of metrics; classification accuracy, precision, recall, F1-score, and ROC curve. After evaluating the developed classifiers, we find that the results obtained from the Decision Tree and SVM have a lower mean square error or a higher accuracy with most of the datasets and are considered to be good classifiers. We find our work to be unique, as we have attempted in our study to provide an overview of the various methods used in the sentiment analysis of text data. We have also built and compared three different classifiers using machine learning techniques to five different datasets of varying sizes and domains.
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