Emo2Val: Inferring Valence Scores from fine-grained Emotion Values
Authors: Alessandro Bondielli, Lucia C. Passaro, Alessandro Lenci (Università di Pisa, 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper studies the relationship between the valence, one of the psycholinguistic variables in the Italian version of ANEW AUTHOR, and emotive scores calculated by exploiting distributional methods AUTHOR. We show two methods to infer valence from fine grained emotions and discuss their evaluation.

Introduction
Recent years have seen a surge in studies concerning emotional ratings, both in psycholinguistics and in affective computing. Traditionally, the three main behavioral dimensions to measure the emotional value of a word are valence, arousal and dominance. AUTHOR define valence as the ``pleasantness of the stimulus'', usually ranging from 1 (very unpleasant) to 9 (very pleasant). The word dead has a low valence rating, whereas holiday has a higher one. Arousal is the intensity of the feeling evoked on a scale from ``stimulated'' to ``unaroused''. A highly stimulating word is passion. On the contrary, sleep is not arousing. Finally, dominance is identified with the degree to which the stimulus makes the reader feel ``in control'' AUTHOR. Victory is a word with high dominance. In the domain of Affective Computing, the goal moves from the identification of such variables to the annotation of the texts with the emotions they express and - for Sentiment Analysis - with their degree of positivity and/or negativity. The aim of this work is to study the relationship between the most important psycholinguistic variables and emotive scores calculated by exploiting distributional methods. In particular, we will focus on valence ratings, assuming that, within these three dimensions, valence is the most highly related with a positive, negative or neutral emotional content. In fact, it can be defined as the ``the polarity of emotional activation'' AUTHOR. A possible approach to infer the valence of the words from co-occurrence statistics is the one adopted by AUTHOR, who followed a bootstrapping method to extend the ANEW lexicon AUTHOR. Another approach would be to exploit a resource such as SenticNet AUTHOR to infer valence based on values of polarity for words or conceptual primitives. An alternative strategy is to infer the valence from an emotive lexicon such as ItEM AUTHOR, a distributional lexicon for Italian, in which words are associated with an emotive score for 8 different emotions. In our opinion, this solution has several advantages: first of all, ItEM has been proven to be quite robust, and guarantees high coverage over Italian words; secondly, it is not only a static resource, but it can be easily expanded with new words, allowing for a quick adaptation to different contexts. Finally, associating words with fine-grained emotional values allows for a wide range of analyses, such as for instance hate and violence detection in texts. Experimental results showed, in an indirect way, that distributional emotive ratings can be very useful in the implementation of systems for polarity classification AUTHOR. However, what is the real relation between emotive scores and valence? Our hypothesis is that emotions can be seen as a representation of valence on a more granular scale. The Plutchik's emotion taxonomy AUTHOR is partitioned into positive or negative emotions. However, borderline emotions such as \textsc{Surprise} are harder to be included into a positive or negative class, and therefore to be attributed with a direct valence rating. Words like party and gun will have widely differing valence ratings, but both strongly elicit the emotion of \textsc{Surprise}. Hence it is interesting to ask the following question: given ItEM, are we able to predict the valence (i.e., positivity and/or negativity) of its words? In order to address this latter point, we performed a simple regression model to predict the valence ratings of words in ANEW AUTHOR given the respective emotive values in ItEM AUTHOR. This paper is organized as follow: in Section sec:resources we describe the resources used for the creation of the model. Section sec:From fine-grained Emotion Values to Polatity shows our method and the results obtained. Finally, in Section sec:resultAndDiscussion we evaluate the results and discuss our findings.

Conclusion
In this work we studied the relationship between valence and distributional emotive scores. We modeled our data with regression in order to predict both a continuous score for valence and its corresponding binarized version (i.e., polarity). Despite the difficulties of modeling an accurate representation of a continuous valence rating from a small and unbalanced dataset like the Italian ANEW, we can identify a clear relationship between distributional emotional scores and a discrete valence obtained by categorizing the ratings into a positive and a negative class. In the near future, we plan to improve our regression models, with the aim of reducing the impact of the distribution of the data in ANEW, possibly implementing new strategies able to cope with non linear data. ANEW is a highly renown psycholinguistic dataset, but we plan to extend the present work to predict sentiment polarity scores taken from SentiWordNet AUTHOR, thereby exploiting the larger coverage of this resource. Moreover, we plan to follow the approach employed in ItEM to create a polarity lexicon for Italian, using ANEW words as seed to build positive and negative polarity centroids. This would also be beneficial for evaluating performances on a emotion-based approach and a polarity-based one. Finally, we aim at testing the effectiveness of our system for Sentiment Polarity Classification.