Indian Journal of Science and Technology
Year: 2018, Volume: 11, Issue: 24, Pages: 1-13
Sudhir Kumar Sharma1 * and Ximi Hoque2
1 Institute of Information Technology & Management, Institutional Area, Janakpuri, New Delhi – 110058, Delhi, India; [email protected]
2KIIT College of Engineering, Gurgaon – 122102, Haryana, India; [email protected]
*Author for correspondence
Sudhir Kumar Sharma,
Institute of Information Technology & Management, Institutional Area, Janakpuri, New Delhi – 110058, Delhi, India; [email protected]
Objectives: This paper analyzes odd-even traffic scheme using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event using hashtags and mentions. The tweets posted publicly can be viewed by anyone interested. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using machine learning classification techniques to classify unseen tweets on the same context. Methods/Analysis: This paper collects tweets on this event under hashtags. This study explores Dandelion Application Programming Interface for annotation of tweets for academic research. This paper uses machine learning classifications approaches for sentiment analysis and opinion mining. This paper presents empirical comparison of three supervised classification algorithms namely, Multinomial Naïve Bayes, Support Vector Machines (SVM) and Multiclass Logistic Regression. The performances of these classifiers are evaluated through standard evaluation metrics. Findings: The experimental results reveal that SVM classifier outperforms the other two classification algorithms. This study may help in decision making of this event to some extent. Application: A large number of applications of sentiment and opinion mining can be designed using packages and freely open resources within a time frame now a days.
Keywords: Hashtag, Odd-even Scheme, Opinion Mining, Sentiment Analysis
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