International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Impact Analysis of Features on Classification for Sentiment Analysis of Tweets

Author : Jasleen Kaur ¹ Sukhjit Singh Sehra ² SumeetKaurSehra ³

Date of Publication :7th June 2016

Abstract: Sentiment Analysis is a sub domain in computational linguistics and information retrieval that is concerned with the topic as well as opinion expressed by a document. Prompted by the growth in use of Twitter, Companies are seeking new ways to extract the opinion expressed by people about their products and services. Tweets represent abridged thoughts which pose a challenge for excavation due to the incredible breadth of covered topic. This paper presents an impact analysis of selection of features on the performance of classifiers for opinion mining of tweets. A model is formulated for classification of tweets and comparison of various existing approaches. N grams approach is used for feature extraction and tf-idf is used as weighing criteria. Maximum accuracy is achieved by employing a hybrid method of SVM and Naïve Bayes attaining 79.80% accuracy employing unigrams and 82.60% employing bigrams as features.

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