Techniques for Sentiment Analysis and Topic Detection of Spanish Tweets: Preliminary Report

Abstract

Sentiment analysis and topic detection are new problems that are at the intersection of natural language processing(NLP) and data mining. Sentiment analysis attempts to determine if a text is positive, negative, or neither, while topic detection attempts to identify the subject of the text. A significant amount of effort has been invested in constructing effective solutions for these problems, mostly for English texts. Using a corpus of Spanish tweets,we present a comparative analysis of different approaches and classification techniques for these problems.The data is preprocessed using techniques and tools proposed in the literature,together with others specifically proposed here that take into account the characteristics of Twitter.Then,popular classifiers have been used.(In particular,all classifiers of WEKA have been evaluated.)Due to its high number not all the results obtained will be presented here.