Deep Learning for Social Sensing from Tweets
Authors: Giuseppe Attardi, Laura Gorrieri, Alessio Miaschi, Ruggero Petrolito (Università di Pisa, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In recent years computational linguistics has seen a rising interest in subjectivity, opinions, feelings and emotions. Even though great attention has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper, we present a methodology to build emotive lexicons by jointly exploiting vector space models and human annotation, and we provide the first results of the evaluation with a crowdsourcing experiment.

Introduction
In recent years, computational linguistics has seen a rising interest in subjectivity, opinions, feelings and emotions. Such a new trend is leading to the development of novel methods to automatically classify the emotions expressed in an opinionated piece of text (for an overview, see Liu, 2012; Pang and Lee, 2008), as well as to the building of annotated lexical resources like SentiWordNet (Esuli and Sebastiani, 2006; Das and Bandyopadhyay, 2010), WordNet Affect (Strapparava and Valitutti, 2004) or EmoLex (Mohammad and Turney, 2013). Emotion detection can be useful in several applications, e.g. in Customer Relationship Management (CRM) it can be used to track sentiments towards companies and their services, products or others target entities. Another kind of application is in Government Intelligence, to collect people’s emotions and points of views about government decisions. The common trait of most of these approaches is a binary categorization of emotions, articulated along the key opposition between positive and negative emotions. Typically, then, these systems would associate words like “rain” and “betray” to the same emotion class in that they both evoke negative emotions, without further distinguishing between the sadness-evoking nature of the former and the anger-evoking nature of the latter. Emotion lexica, in which lemmas are associated to the emotions they evoke, are valuable resources that can help the development of detection algorithms, for instance as knowledge sources for the building of statistical models and as gold standards for the comparison of existing approaches. Almost all languages but English lack a high-coverage high-quality emotion inventory of this sort. Building these resources is very costly and requires a lot of manual effort by human annotators. On the other hand, connotation is a cultural phenomenon that may vary greatly between languages and between different time spans (Das and Bandyopadhyay, 2010), so that the simple transfer of an emotive lexicon from another language cannot be seen as nothing else than a temporary solution for research purposes. Crowdsourcing is usually able to speed the process and dramatically lower the cost of human annotation (Snow et al., 2008; Munro et al, 2010). Mohammad and Turney (2010, 2013) show how the “wisdom of the crowds” can be effectively exploited to build a lexicon of emotion associations for more than 24,200 word senses. For the creation of their lexicon, EmoLex, they selected the terms from Macquarie Thesaurus (Bernard, 1986), General Inquirer (Stone et al.,1966), WordNet Affect Lexicon (Strapparava and Valitutti., 2004) and Google n-gram corpus (Brants and Franz, 2006) and they exploited a crowdsourcing experiment, in order to obtain, for every target term, an indication of its polarity and of its association with one of the eight Plutchik (1994)’s basic emotions (see below). The methodology proposed by Mohammad and Turney (2010, 2013), however, cannot be easily exported to languages where even small emotive lexica are missing. Moreover, a potential problem of a lexicon built solely on crowdsourcing techniques is that its update requires a re-annotation process. In this work we’re proposing an approach to address these issues by jointly exploiting corpus-based methods and human annotation. Our output is ItEM, a high-coverage emotion lexicon for Italian, in which each target term is provided of an association score with eight basic emotion. Given the way it is built, ItEM is not only a static lexicon, since it also provides a dynamic method to continuously update the emotion value of words, as well as to increment its coverage. This resource will be comparable in size to EmoLex, with the following advantages: i) minimal use of external resources to collect the seed terms; ii) little annotation work is required to build the lexicon; iii) its update is mostly automatized. This paper is structured as follows: In section 2, we present ItEM by describing its approach to the seed collection and annotation step, its distributional expansion and its validation. Section 3 reports the results obtained from the validation of the resource using a crowdsourcing experiment.

Conclusion
What we are proposing with ItEM is a reliable methodology that can be very useful for languages that lack lexical resources for emotion detection, and that is at the same time scalable and reliable. Moreover, the resulting resource can be easily updated by means of fully automatic corpus-based algorithms that do not require further work by human annotators, a vantage that can turn out to be crucial in the study of a very unstable phenomenon like emotional connotation. The results of the evaluation with crowdsourcing show that a seed-based distributional semantic model is able to produce high quality emotion scores for the target words, which can also be used to dynamically expand and refine the emotion tagging process.