Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres

Automatic Detection of Modality with ITGETARUNS

In this paper we present a system for modality detection which is then used for Subjectivity and Factuality evaluation. The system has been tested lately on a task for Subjectivity and Irony detection in Italian tweets , where the performance was 10th and 4th, respectively, over 27 participants overall. We will focus our paper on an internal evaluation where we considered three national newspapers Il Corriere, Repubblica, Libero. This task was prompted by a project on the evaluation of press stylistic features in political discourse. The project used newspaper articles from the same sources over a period of three months, thus including latest political 2013 governmental crisis. We intended to produce a similar experiment and evaluate results in comparison with previous 2011 crisis. In this evaluation, we focused on Subjectivity, Polarity and Factuality which include Modality evaluation. Final graphs at the end of the paper will show results confirming our previous findings about differences in style, with Il Corriere emerging as the most atypical