Sentiment Analysis for Urdu News Tweets Using Decision Tree
Raheela Bibi, Usman Qamar, Munazza Ansar, Asma Shaheen · 2019
In the last few years, with rapid growth in use of networking sites such as twitter and Facebook has been increased greatly. This also attracted the researcher to use social networks data for sentiment analysis. Sentiment analysis is also known as opinion mining is the process of finding out the emotion such as positive, negative and neutral from the series of words. In present, on internet huge amount of data has been generated and to extract useful information from data is also become interest for the researchers. Sentiment analysis has been done mostly in English and Chinese languages. In this paper, sentiment classification is done on Urdu news tweets. The proposed methodology consists upon two steps. In first step data preprocessing is done such as removal of hash tag and removal of stop words is done. In second step feature vector is designed. The feature vector is formulated by through the identification of number of positive words, negative words, and presence of negation and use of POS tags. After formulation of feature vector the decision tree is used as classification algorithm. The decision tree classifies the tweet as positive, negative and neutral. The experimental result of the proposed methodology shows significant success in terms of accuracy and sentiment analysis.