Geometric and statistical analysis of emotions and topics in corpora
Authors: Francesco Tarasconi, Vittorio Di Tomaso (CELI Language Technology, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
NLP techniques can enrich un-structured textual data, detecting topics of interest and emotions. The task of under-standing emotional similarities between different topics is crucial, for example, in analyzing the Social TV landscape. A measure of how much two audiences share the same feelings is required, but also a sound and compact representation of these similarities. After evaluating differ-ent multivariate approaches, we achieved these goals by adapting Multiple Corre-spondence Analysis (MCA) techniques to our data. In this paper we provide back-ground information and methodological reasons to our choice. We also provide an example of Social TV analysis, performed on Twitter data collected between October 2013 and February 2014.

Introduction
Classification of documents based on topics of interest is a popular NLP research area; see, for example, Hamamoto et al. (2005). Another important subject, especially in the context of Web 2.0 and social media, is the sentiment analysis, mainly meant to detect polarities of expressions and opinions (Liu, 2012). A sentiment analysis task which has seen less contributions, but of growing popularity, is the study of emotions (Wiebe et al., 2005), which requires introducing and analyzing multiple variables (appropriate ”emotional dimensions”) potentially correlated. This is especially important in the study of the so-called Social TV (Cosenza, 2012): people can share their TV experience with other viewers on social media using smartphones and tablets. We define the empirical distribution of different emotions among viewers of a specific TV show as its emotional profile. Comparing at the same time the emotional profiles of several formats requires appropriate descriptive statistical techniques. During the research we conducted, we evaluated and selected geometrical methods that satisfy these requirements and provide an easy to understand and coherent representation of the results. The methods we used can be applied to any dataset of documents classified based on topics and emotions; they also represent a potential tool for the quantitative analysis of any NLP annotated data. We used the Blogmeter platform1 to download and process textual contents from social networks (Bolioli et al., 2013). Topics correspond to TV programs discussed on Twitter. Nine emotions are detected: the basic six according to Ekman (Ekman, 1972) (anger, disgust, fear, joy, sadness, surprise), love (a primary one in Parrot’s classification) and like/dislike expressions, quite common on Twitter.

Conclusion
By applying carefully chosen multivariate statistical techniques, we have shown how to represent and highlight important emotional relations between topics. Further results in the MCA field can be experimented on datasets similar to the ones we used. For example, additional information about opinion polarity and document authors (such as Twitter users) could be incorporated in the analysis. The geometric approach to MCA (Le Roux and Rouanet, 2004) could be interesting to study in greater detail the clouds of impressions and documents (J and D matrices); authors could also be considered as mean points of well-defined subclouds.