Simple Learner Based Polarity Prediction on Twitter Data

Volume 4 Issue 3 September - November 2016

Research Paper

Simple Learner Based Polarity Prediction on Twitter Data

Megha Mishra*, Dolly Khandelwal**, Vishnu Kumar Mishra***

* Senior Assistant Professor, Department of Computer Science and Engineering, SSTC (SSGI), CSVTU, Bhilai, Chattisgarh, India.

** PG Scholar, Department of Computer Science and Engineering, SSTC (SSGI), CSVTU, Bhilai, Chattisgarh, India.

*** Associate Professor, Bharti College of Engineering and Technology, Durg, Chattisgarh, India.

Mishra, M., Khandelwal, D., and Mishra, V.K. (2016). Simple Learner Based Polarity Prediction On Twitter Data. i-manager’s Journal on Computer Science, 4(3), 24-28. https://doi.org/10.26634/jcom.4.3.8287

Abstract

Sentiment analysis is the process of extracting, understanding and analyzing the opinions expressed by the people using machine learning. In recent years, the increase in social networking sites has brought a new way of collecting information worldwide. Twitter is the famous microblogging site which allows millions of users to share their opinions on a wide variety of topics on daily basis. The posts are called tweets which are confined to 140 words. These opinions are important for researchers for analysis and efficient decision making. So, Sentiment analysis helps to extract the clear insight from social media. In this paper, the authors have presented an approach that classifies the sentences into categories as positive, negative, or neutral. For polarity classification, three multidimensional fields used are politics, companies, and entertainment. These fields give a better reflection of what is happening around the world. The dataset is extracted from twitter using the Twitter API. The API is created using the tweepy – the python library. The machine learning classifiers used are Naive Bayes, Baseline, and Maximum Entropy. The feature extraction is done using the unigram approach and the performance of different classifiers are compared.