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Authors: Sandro Pezzelle, Ionut Sorodoc, Aurélie Herbelot, Raffaella Bernardi (Università di Trento, 2016)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
A crucial point for the applicability of sentiment analysis on Twitter is the level of manual intervention necessary to adapt the approach to the domain considered. In this work we propose a new classification of sentiment polarity that exploits the barrier features, originally introduced for the classification of relationships extracted from texts. Empirical data sets tests used in the SemEval2014 competition show that the proposed approach exceeds the performance of baseline systems in most cases.

Introduction
Sentiment analysis (SA) AUTHOR, or opinion mining, is mainly about finding out the feelings of people from data such as product reviews and news articles. Most methods adopt a two-step strategy for SA AUTHOR: in the subjectivity classification step, the target is classified to be subjective or neutral (objective), while in the polarity classification step the subjective targets are further classified as positive or negative. Therefore, two classifiers are trained for the whole SA process: the subjectivity classifier and the polarity classifier. Polarity is an aspect of sentiment analysis which can be faced as a three-way classification problem, in that it aims to associate either a positive, negative or neutral polarity to each tweet. Expressions in tweets are often ambiguous because they are very informal messages no longer than 140 characters, containing a lot of misspelled words, slang, modal particles and acronyms.The characteristics of the employed language are very different from more formal documents and we expect statistical methods trained on tweets to perform well thanks to an automatic adaptation to such specifications. As identified by tasks included in competitions AUTHOR and AUTHOR, twitter sentiment analysis is a relevant topic for scientific research. To the best of our knowledge AUTHOR present a comprehensive, State-of-the-Art (SoA) review on the research work done in various aspects of SA. Furthermore some approaches, as described in AUTHOR, are based on the combination of several existing SoA "off-the-shelf'' methods for sense-level sentiment analysis. AUTHOR proposes an approach based on the notion that the sentiment of a term depends on its contextual semantics and some trigonometric properties on SentiCircles, that is a 2D geometric circle. These properties are applied to amend an initial sentiment score of terms, according to the context in which they are used. The sentiment identification at either entity or tweet-level is then performed by levering trigonometric identities on SentiCircles. The approach we are proposing has been experimentally assigned by comparing its performance with two baseline systems. In addition to that, the capacity of adaptation of the approach to slightly different domains has been tested by comparison on a web-blog data set the performances of two systems in which the Barrier Feature(BF) dictionary has been historically built on a collection of tweets and Wikipedia webpages. Eventually, the contribution of BF has been evaluated.

Conclusion
We explored the effectiveness of BFs for sentiment polarity classification in Twitter posts and we showed on SemEval2014 data sets that they can be very effective. In our approach, the need of a manual intervention is really minimum. Indeed, the BFs dictionary can be built from any collection of tweets, even one that do not belong to the same domain of the considered task. This is quite interesting because it suggests that BFs are able to capture hints about the polarity of the expressions in a domain independent way. \hide{ Interestingly, the considered training set is not very large. This means that ones the BF dictionary is built in an unsupervised manner, a classier based on such features do not need a large training set to attack good performance. Last, but not least, we intend to further explore the LiveJournal2014 data set by collecting a weblog corpus from the web. Although this is not a straightforward task, such a data set would allow us to verify whether we can obtain a better performance on LiveJournal2014 data set when the BF dictionary is built on a corpus made of the same kind of texts, even if they consider different subjects.