Developing corpora and tools for sentiment analysis: the experience of the University of Turin group
Authors: Manuela Sanguinetti, Emilio Sulis, Viviana Patti, Giancarlo Ruffo, Leonardo Allisio, Valeria Mussa, Cristina Bosco (Università di Torino, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The paper describes the ongo-ing experience at the University of Turin in developing linguistic resources and tools for sentiment analysis of social media. We describe in particular the development of Senti-TUT, a human annotated cor-pus of Italian Tweets including labels for sentiment polarity and irony, which has been recently exploited within the SENTI-ment POLarity Classification shared task at Evalita 2014. Furthermore, we report about our ongoing work on the Felicittà web-based platform for estimating happi-ness in Italian cities, which provides vi-sualization techniques to interactively ex-plore the results of sentiment analysis per-formed over Italian geotagged Tweets.

Introduction
1 Introduction Several efforts are currently devoted to automati-cally mining opinions and sentiments from natu-ral language, e.g. in social media posts, news and reviews about commercial products. This task en-tails a deep understanding of the explicit and im-plicit information conveyed by the language, and most of the approaches applied refer to annotated corpora and adequate tools for their analysis. In this paper, we will describe the experiences carried on at the Computer Science Department of the University of Turin in the development of cor-pora and tools for Sentiment Analysis and Opin-ion Mining (SA&OM) during the last few years. These experiences grew and are still growing in a scenario where an heterogeneous group of re-searchers featured by skills varying from compu-tational linguistics, sociology, visualization tech-niques, big data analysis and ontologies cooper-ates. Both the annotation applied in the devel-oped corpora and the tools for analyzing and dis-playing data analysis depend in fact on a cross-fertilization of different research areas and on the expertise gained by the group members in their re-spective research fields. The projects we will de-scribe are currently oriented to the investigation of aspects of data analysis that can be observed in such a particular perspective, e.g. figurative lan-guage or disagreement deep analysis, rather than to the achievement of high scores in the applica-tion of classifiers and statistical tools. The paper is organized as follows. The next section provides an overview on the annotated corpus Senti-TUT, which includes two main datasets: TW-NEWS (political domain) and TWFELICITTA (generic collection), while Section 3 describes the main uses of Senti-TUT and the Felicittà application context.

Conclusion
The paper describes the experiences done at the University of Turin on topics related to SA&OM, with a special focus on the main directions we are following. The first one is the development of an-notated corpora for Italian that can be exploited both in automatic systems’ training, in evaluation fora, and in investigating the nature of the data it-self, also by a detailed analysis of the disagree-ment occurring in the datasets. The second di-rection, which is exemplified by ongoing work on the Felicittà platform, consists in the devel-opment of applications of SA on social media in the social and behavioral sciences field, where SA techniques can contribute to interpret the degree of well-being of a country (Mitchell et al., 2013; Quercia et al., 2012), with a special focus on displaying the results generated by the analysis in a graphic form that can be easily readable also for non-expert users.