Deep Tweets: from Entity Linking to Sentiment Analysis
Authors: Pierpaolo Basile, Valerio Basile, Malvina Nissim, Nicole Novielli (Università di Bari Aldo Moro, University of Groningen, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The huge amount of information streaming from online social networking is increasingly attracting the interest of researchers on sentiment analysis on micro-blogging platforms. We provide an overview on the open challenges of sentiment analysis on Italian tweets. We discuss methodological issues as well as new directions for investigation with particular focus on sentiment analysis of tweets containing figurative language and entitybased sentiment analysis of micro-posts.

Introduction
Flourished in the last decade, sentiment analysis is the study of the subjectivity and polarity (positive vs. negative) of a text (Pang and Lee, 2008). Traditionally, sentiment analysis techniques have been successfully exploited for opinionated corpora, such as news (Wiebe et al., 2005) or reviews (Hu and Liu, 2004). With the worldwide diffusion of social media, sentiment analysis on micro-blogging (Pak and Paroubek, 2010) is now regarded as a powerful tool for modelling socioeconomic phenomena (O’Connor et al., 2010; Jansen et al., 2009). The success of the tasks of sentiment analysis on Twitter at SemEval since 2013 (Nakov et al., 2013; Rosenthal et al., 2014; Rosenthal et al., 2015) attests this growing trend (on average, 40 teams per year participated). In 2014, Evalita also successfully opens a track on sentiment analysis with SENTIPOLC, the task on sentiment and polarity classification of Italian Tweets (Basile et al., 2014). With 12 teams registered, SENTIPOLC was the most popular task at Evalita 2014, confirming the great interest of the NLP community in sentiment analysis on social media, also in Italy. In a world where e-commerce is part of our everyday life and social media platforms are regarded as new channels for marketing and for fostering trust of potential customers, such great interest in opinion mining from Twitter isn’t surprising. In this scenario, what is also rapidly gaining more and more attention is being able to mine opinions about specific aspects of objects. Indeed, interest in Aspect Based Sentiment Analysis (ABSA) is increasing, and SemEval dedicates now a full task to this problem, since 2014 (Pontiki et al., 2014). Given a target of interest (e.g., a product or a brand), ABSA traditionally aimed at summarizing the content of users’ reviews in several commercial domains (Hu and Liu, 2004; Ganu et al., 2013; Thet et al., 2010). In the context of ABSA, an interesting task is represented by finer-grained assignment of sentiment to entities. To this aim, mining information from microblogging platforms also involves reliably identifying entities in tweets. Hence, entity linking on twitter is gaining attention, too (Guo et al., 2013). Based on the above observations, we discuss open issues in Section 2. In Section 3, we propose an extension of the SENTIPOLC task for Evalita 2016 by also introducing entity-based sentiment analysis as well as polarity detection of messages containing figurative language. Finally, we discuss the feasibility of our proposal in Section 4.

Conclusion
To conclude, we presented the entity linking and sentiment analysis tasks as related to one another, as shown in the pipeline in Figure 1, specifying that participants will be able to choose which portions of the tasks they want to concentrate on. Additionally, we would like to stress that this proposal could also be conceived as two entirely separate tasks: one on sentiment analysis at the entity level, including entity detection and linking, and one on sentiment analysis at the message level, including the detection of figurative readings, as a more direct follow-up of SENTIPOLC.