A Multi-View Sentiment Corpus

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 273–280, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics A Multi-View Sentiment Corpus Debora Nozza Elisabetta Fersini Enza Messina University of Milano-Bicocca, Viale Sarca 336, 20126, Milan, Italy {debora.nozza, fersini, messina}@disco.unimib.it Abstract Sentiment Analysis is a broad task that in- volves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usu- ally investigate independently each aspect, i.e. Subjectivity Classification, Sentiment Polarity Classification, Emotion Recogni- tion, Irony Detection. In this paper we present a Multi-View Sentiment Corpus (MVSC), which comprises 3000 English microblog posts related the movie domain. Three independent annotators manually labelled MVSC, following a broad anno- tation schema about different aspects that can be grasped from natural language text coming from social networks. The con- tribution is therefore a corpus that com- prises five different views for each mes- sage, i.e. subjective/objective, sentiment polarity, implicit/explicit, irony, emotion. In order to allow a more detailed investi- gation on the human labelling behaviour, we provide the annotations of each human annotator involved. 1 Introduction The exploitation of user-generated content on the Web, and in particular on the social media plat- forms, has brought to a huge interest on Opin- ion Mining and Sentiment Analysis. Both Natural Language Processing (NLP) communities and cor- porations are continuously investigating on more accurate automatic approaches that can manage large quantity of noisy natural language texts, in order to extract opinions and emotions towards a topic. The data are usually collected from Twitter, the most popular microblogging platform. In this particular environment, the posts, called tweets, are constrained to a maximum number of char- acters. This constraint, in addition to the social media context, leads to a specific language rich of synthetic expressions that allow the users to ex- press their ideas or what happens to them in a short but intense way. However, the application of automatic senti- ment classification approaches, in particular when dealing with noisy texts, is subjected to the pres- ence of sufficiently manually annotated dataset to perform the training. The majority of the corpora available in the literature are focused on only one (or at most two) aspects related to Sentiment Anal- ysis, i.e. Subjectivity, Polarity, Emotion, Irony. In this paper we propose a Multi-View Senti- ment Corpus1, manually labelled by three inde- pendent annotators, that makes possible to study Sentiment Analysis by considering several aspects of the natural language text: subjective/objective, sentiment polarity, implicit/explicit, irony and emotion. 2 State of the Art The work of Go et al. (2009) was the first at- tempt to address the creation of a sentiment cor- pus in a microblog environment. Their approach, introduced in (Read, 2005), consisted to filter all the posts containing emoticons and subsequently label each post with the polarity class provided by them. For example, :) in a tweet indicates that the tweet contains positive sentiment …

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