Sentiment Analysis in Social Media Texts

Abstract

This paper presents a method for sentiment analysis specifically designed to work with Twitter data (tweets), taking into account their structure, length and specific language. The approach employed makes it easily extendible to other languages and makes it able to process tweets in near real time. The main contributions of this work are: a) the pre-processing of tweets to normalize the language and generalize the vocabulary employed to express sentiment; b) the use minimal linguistic processing, which makes the approach easily portable to other languages; c) the inclusion of higher order n-grams to spot modifications in the polarity of the sentiment expressed; d) the use of simple heuristics to select features to be employed; e) the application of supervised learning using a simple Support Vector Machines linear classifier on a set of realistic data. We show that using the training models generated with the method described we can improve the sentiment classification performance, irrespective of the domain and distribution of the test sets.