Towards an Italian Lexicon for Polarity Classification (polarITA):,a Comparative Analysis of Lexical Resources for Sentiment Analysis
Authors: Delia Irazù Hernàndez Farìas, Irene Laganà, Viviana Patti, Cristina Bosco (Universitat Politècnica de València, Università di Pavia, Università di Torino, 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The paper describes a preliminary study for the development of a novel lexicon for Italian sentiment analysis, i.e. where words are associated with polarity values. Given the influence of sentiment lexica on the performance of sentiment analysis systems, a methodology based on the detection and classification of errors in existing lexical resources is proposed and an extrinsic evaluation of the impact of such errors is applied. The final aim is to build a novel resource from the filtering applied to the existing lexical resources, which can integrate them with missing lexical entries and more reliable associations of polarity with entries.

Introduction
Sentiment Analysis (SA), described as the task of automatically determine the polarity in a given piece of text AUTHOR, is currently among the most widely investigated topics within NLP. Overall, the approaches for addressing such task are mainly based on techniques ranging from traditional machine learning to novel deep learning ones, as it can be seen also in the context of shared tasks on sentiment polarity classification in Twitter recently proposed, respectively for English AUTHOR and Italian AUTHOR, within the SemEval and Evalita periodical evaluation campaigns. Moreover, the detection of specific words associated with polarity values or emotions has been considered as a powerful information source for identifying the sentiment behind a text. Among the resources which are more commonly exploited by SA systems for performing their task there are therefore sentiment lexica, i.e., lists of words with associated polarity values or emotions. Several techniques have been applied for the development of lexical resources for SA: they can be built from scratch, manually or automatically, or extracted from corpora AUTHOR. Nevertheless, the vast majority of these resources are written in English, and a lack of resources currently features several other languages. One of the most commonly applied alternatives for having resources in language other than English is to automatically translate some available English lexicon via tools such as Google translate }. But there are many constraints involved in this kind of process, such as handling synonyms and polysemous words, multi-word expressions, but also to deal with cultural differences between source and target language. Apart from this, possible variations of polarity across different contexts and languages should be carefully taken into account, while such approaches rely somehow on the assumption that affective norms related to sentiment are stable across languages. In this paper we are interested into evaluate the reliability of the lexical resources currently available for Italian SA and, providing that the most of them are obtained by translation, we will mainly focus on the reliability of automatically translating English resources to Italian language. For doing so, we carried out a methodology involving different facets. %\\ Our final aim is to develop a new SA resource for Italian, which comprises pre-existing translated lexical entries enriched with the manual correction of the polarity assigned, as resulting from our analysis, but also includes entries which are featured by a polarity but are missing in the available lexica. The paper is organized as follows. In the next section, we describe our methodology which mainly consists in three steps: the selection of a sample of tweets from an Italian sentiment corpus and exploited as part of the gold standard in the Sentipolc@Evalita2016 shared task AUTHOR; automatic extraction of the lexical entries polarized according to a set of benchmark sentiment lexica for Italian; the analysis of these entries and the comparison with those expected by a human judge. Section three shows instead an extrinsic evaluation of the impact of the detected errors on the results of the SA system. Some hints about future development of this research are given in the conclusion.

Conclusion
In this paper we propose the preliminary investigation about a methodology for the development of a novel lexical resource for Italian SA, namely polarITA, which takes advantage of the analysis and filtering of errors occurring in the available lexical resources. We carried out a manual analysis of a set of tweets for determining the reliability of sentiment-related lexica, showing that, even if the transfer of lexical information between two different languages is a common practice to address the lack of resources, information related to sentiment is lost during it. The identified errors are then exploited as a starting point for developing the novel resource.\\ As future work, we are planning to extend the resource in several directions: by investigating multi-word expressions, extending the coverage to a larger corpus, exploring the impact of figurative language devices such as irony and sarcasm in the use of certain polarized words AUTHOR. Moreover, our future effort will be oriented to the automatization of a larger part of the methodology and its application to other languages currently under resourced.