Sentiment analysis system for Roman Urdu

Abstract

Sentiment analysis is a computational process to identify positive or negative sentiments expressed in a piece of text. In this paper, we present a sentiment analysis system for Roman Urdu. For this task, we gathered Roman Urdu data of 779 reviews for five different domains, i.e., Drama, Movie/Telefilm, Mobile Reviews, Politics, and Miscellaneous (Misc). We selected unigram, bigram and uni-bigram (unigram + bigram) features for this task and used five different classifiers to compute accuracies before and after feature reduction. In total, thirty-six (36) experiments were performed, and they established that Naïve Bayes (NB) and Logistic Regression (LR) performed better than the rest of the classifiers on this task. It was also observed that the overall results were improved after feature reduction.