Sentiment extraction from tweets: Multilingual challenges

Abstract

Every day users of social networks and microblogging services share their point of view about products, companies, movies and their emotions on a variety of topics. As social networks and microblogging services become more popular, the need to mine and analyze their content grows.We study the task of sentiment analysis in the well-known social network Twitter (https://twitter.com/). We present a case study on tweets written in Greek and propose an effective method that categorizes Greek tweets as positive, negative and neutral according to their sentiment. We validate our method’s effectiveness on both Greek and English to check its robustness on multilingual challenges, and present the first multilingual comparative study with three pre-existing state of the art techniques for Twitter sentiment extraction on English tweets. Last but not least, we examine the importance of different preprocessing techniques in different languages. Our technique outperforms two out of the three methods we compared against and is on a par to the best of those methods, but it needs significantly less time for prediction and training.