A context based model for Twitter Sentiment Analysis in Italian
Authors: Andrea Vanzo, Giuseppe Castellucci, Danilo Croce, Roberto Basili (Università di Roma Tor Vergata, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribu-tion of this information has been recently investigated for the English language by modeling the polarity detection as a se-quential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian.

Introduction
Web 2.0 and Social Networks allow users to write about their life and personal experiences. This huge amount of data is crucial in the study of the interactions and dynamics of subjectivity on the Web. Sentiment Analysis (SA) is the computational study and automatic recognition of opinions and sentiments. Twitter is a microblogging service that counts about a billion of active users. In Twitter, SA is traditionally treated as any other text classification task, as proved by most systems participating to the Sentiment Analysis in Twitter task in SemEval-2013 (Nakov et al., 2013). A Machine Learning (ML) setting allows to induce detection functions from real world labeled examples. However, the shortness of the message and the resulting semantic ambiguity represent a critical limitation, thus making the task very challenging. Let us consider the following message between two users: Benji: @Holly sono completamente d’accordo con te The tweet sounds like to be a reply to the previous one. Notice how no lexical or syntactic property allows to determine the polarity. Let’s look now at the entire conversation: Benji : @Holly con un #RigoreAl90 vinci facile!! Holly : @Benji Lui vince sempre per`o :) accanto a chiunque.. Nessuno regge il confronto! Benji : @Holly sono completamente d’accordo con te The first is clearly a positive tweet, followed by a positive one that makes the third positive as well. Thus, through the conversation we can disambiguate even a very short message. We want to leverage on this to define a context-sensitive SA model for the Italian language, in line with (Vanzo et al., 2014). The polarity detection of a tweet is modeled as a sequential classification task through the SVMhmm learning algorithm (Altun et al., 2003), as it allows to classify an instance (i.e. a tweet) within an entire sequence. First experimental evaluations confirm the effectiveness of the proposed sequential tagging approach combined with the adopted contextual information even in the Italian language. A survey of the existing approaches is presented in Section 2. Then, Section 3 provides an account of the context-based model. The experimental evaluation is presented in Section 4.

Conclusion
In this work, the role of contextual information in supervised Sentiment Analysis over Twitter is in-vestigated for the Italian language. Experimental results confirm the empirical findings presented in (Vanzo et al., 2014) for the English language. Al-though the size of the involved dataset is still lim-ited, i.e. about 1,400 tweets, the importance of contextual information is emphasized within the considered markovian approach: it is able to take advantage of the dependencies that exist between different tweets in a conversation. The approach is also largely applicable as all experiments have been carried out without the use of any manual coded resource, but mainly exploiting unannotated material within the distributional method. A larger experiment, eventually on an oversized dataset, such as SentiTUT4, will be carried out.