On Evaluating the Contribution of Text Normalisation Techniques to Sentiment Analysis on Informal Web 2.0 Texts

Alejandro Mosquera López, Yoan Gutiérrez, Paloma Moreda

Abstract: The writing style used in social media usually contains informal elements that can lower the performance of Natural Language Processing applications. For this reason, text normalisation techniques have drawn a lot of attention recently when dealing with informal content. However, not all the texts present the same level of informality and may not require additional pre-processing steps. Therefore, in this paper we explore the results of applying lexical normalisation applied to a sentiment analysis classification task on Web 2.0 texts, shows more than a 2.6% improvement over average F1 for the most informal data.