A Graph-based Model of Contextual Information in Sentiment Analysis over Twitter
Authors: Giuseppe Castellucci, Danilo Croce, Roberto Basili (Università di Roma Tor Vergata, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Analyzing the sentiment expressed by short messages available in Social Media is challenging as the information when considering an instance is scarce. A fundamental role is played by Contextual information that is available when interpreting a message. In this paper, a Graph-based method is applied: a graph is built containing the contextual information needed to model complex interactions between messages. A Label Propagation algorithm is adopted to spread polarity information from known polarized nodes to the others.

Introduction
Sentiment Analysis (SA) AUTHOR faces the problem of deciding whether a text expresses a sentiment, e.g. positivity or negativity. Social Media are observed to measure the sentiment expressed in the Web about products, companies or politicians. The interest in the analysis of tweets led to the definition of highly participated challenges, e.g. AUTHOR or AUTHOR. Machine Learning (ML) approaches are often adopted to classify the sentiment AUTHOR, where specific representations and hand-coded resources AUTHOR are used to train some classifier. As tweets are very short, the amount of available information for ML approaches is in general not sufficient for a robust decision. A valid strategy AUTHOR exploits Contextual information, e.g. the reply-to chain, to support a robust sentiment recognition in online discussions. In this paper, we foster the idea that Twitter messages belong to a network where complex interactions between messages are available. As suggested in AUTHOR, tweets can be represented in graph structures, along with words, hashtags or users. A Label Propagation algorithm AUTHOR can be adopted to propagate (possibly noisy) sentiment labels within the graph. In AUTHOR, it has been shown that such approach can support SA by determining how messages, words, hashtags and users influence each other. The definition of the graph is fundamental for the resulting inference, e.g. when mixing messages about different topics, sentiment detection can be difficult. We take inspiration from the contexts defined in AUTHOR. In AUTHOR no explicit relation between messages is considered. We, instead, build a graph where messages in the same context are linked each other and to the words appearing in them. Moreover, we inject prior polarity of words as available in a polarity lexicon AUTHOR. Experiments are carried out over a subset of the Evalita 2014 Sentipolc AUTHOR dataset, showing improvements in the polarity classification with respect to not using networked information. In the remaining, Section sec:approach presents our graph-based approach. In Section sec:exps we evaluate the proposed method with respect to a dataset in Italian and we derive the conclusions in Section sec:conclusion.

Conclusion
In this paper, the Contextual Graph is defined as a structure where messages can influence each other by considering both intra-context and extracontext links: the former are links between messages, while the latter serves to link messages in different contexts through shared words. The application of a Label Propagation algorithm confirms the positive impact of contextual information in the Sentiment Analysis task over Social Media. We successfully injected prior polarity information of words in the graph, obtaining further improvements. This is our first investigation in graph approaches for SA: we only adopted the MAD algorithm, while other algorithms have been defined, since (Zhu and Ghahramani, 2002) and they will be investigated in future works. Moreover, other contextual information could be adopted. Finally, other datasets should be considered, proving the effectiveness of the proposed method that does not strictly depend on the language of messages.