Comparative Study Of Classification Algorithms

3008 Words Sep 4th, 2014 13 Pages
Comparative Study of Classification Algorithms used in Sentiment Analysis
Amit Gupte, Sourabh Joshi, Pratik Gadgul, Akshay Kadam
Department of Computer Engineering, P.E.S Modern College of Engineering
Shivajinagar, Pune amit.gupte@live.com Abstract—The field of information extraction and retrieval has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the polarity of given text using text processing and classification. There are various approaches in the task of classification of text into various classes. Use of particular algorithms depends on the kind of input provided. Analyzing and understanding when to use which algorithm is an important aspect and can help in improving accuracy of results.

Keywords— Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted Trees, Random Forest.
I. INTRODUCTION
In this paper we have presented a comparative study of most commonly used algorithms for sentimental analysis. The task of classification is a very vital task in any system that performs sentiment analysis. We present a study of algorithms viz. 1. Naïve Bayes 2.Max Entropy 3.Boosted Trees and 4. Random Forest Algorithms. We showcase the basic theory behind the algorithms, when they are generally used and their pros and cons. The reason behind selecting only the above mentioned algorithms is the extensive use in various tasks of sentiment analysis. Sentiment analysis of reviews is very common application, the…
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