FB-NEWS15: A Topic-Annotated Facebook Corpus for Emotion Detection and Sentiment Analysis
Authors: Lucia C. Passaro, Alessandro Bondielli, Alessandro Lenci (Università di Pisa, 2016)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper we present the FB-NEWS15 corpus, a new Italian resource for sentiment analysis and emotion detection. The corpus has been built by crawling the Facebook pages of the most important newspapers in Italy and it has been organized into topics using LDA. In this work we provide a preliminary analysis of the corpus, including the most debated news in 2015.

Introduction
The use of Social Networks (SN) platforms like Facebook and Twitter has developed overwhelmingly in recent years. SN are exploited for different purposes ranging from the sharing of contents among friends and useful contacts to the newsgathering about different domains such as politics and sports AUTHOR. Many journalists indeed use SN platforms for professional reasons AUTHOR. Several recent studies provide insights on how the popularity of blogs and other user generated content impacted the way in which news are consumed and reported. AUTHOR states that SN platforms provide an easy and affordable way to take part in discussions with larger groups of people and, consequently, the bond between SN and information is becoming increasingly stronger. Mass information is gradually moving towards general platforms, and official websites are losing their lead position in providing information. As noted by AUTHOR, even though the use of internet in the years 2009-2012 has grown, the same is not reflected in the consumption of online newspapers, probably because of the increasing use of SN for news diffusion and gathering. If on the one hand this apparent decline of the traditional news platforms may lead to a decline in quality and news coverage AUTHOR, on the other hand the rise of SN as platforms to spread news promotes a more fervid debate between users AUTHOR. This issue is central for the present work. In fact, user's comments very often contain their own opinions about a certain issue. In addition, because of the colloquial style of the comments, they contain large amounts of words and collocations with a high subjective content, mostly concerning the author's emotive stance. Facebook is one of the most popular online SN in the world with 1 billion active users per month and it offers the possibility to collect data from people of different ages, educational levels and cultures. From a linguistic point of view, previous studies AUTHOR demonstrated that the language in Facebook is more emotional and interpersonal compared for example to the language in Twitter. Probably, this is due to the fact that in Facebook there is a stronger psychological closeness between the author and audience because of the different structure (bidirectional vs. unidirectional graphs) of the SNs. In this paper we present the FB-NEWS15 corpus, a new Italian resource for sentiment analysis and emotion detection. The FB-NEWS15 corpus can be freely downloaded at under the Creative Commons Attribution License. The debate among users in commenting news and posts on Facebook offers a lot of subjective material to study the way in which people express their own opinions and emotions about a target event. In fact, in FB-NEWS15 we find linguistic items expressing the whole range of positive and negative emotions. In analyzing a news corpus, however, it is not simple to aggregate the posts on the basis of a certain fact, since several posts relate to the same event. For this reason, we decided to organize the corpus into clusters of topically related news identified with Latent Dirichlet Allocation (LDA: AUTHOR). This approach allow us to infer the most debated news in the corpus, and, in a second step, to discover the readers' sentiment about a particular topic. The paper is organized as follows: Section fb-news15 describes the creation of the corpus, from crawling (crawling) to linguistic annotation (taln), and finally provides basic corpus statistics (analysis). Section topics reports on the automatic topic extraction with LDA.

Conclusion
As one of the most widespread social networks, Facebook offers the possibility to collect opinionated pieces of texts from people of different ages, cultures and education. The composition of FB-NEWS15, in which each comment is explicitly associated with a particular post, allows us to study the differences in terms of readers' perceptions about a particular topic. Differently from other social media like Twitter, Facebook contains larger texts including lot of subjective expressions that are very useful for the construction of sentiment and emotive lexicons. Starting from previous works AUTHOR, we plan to use this corpus to build lexical resources for sentiment analysis and emotion detection, which will include both words and complex terms. In addition, we plan to optimize the topic modeling phase and to investigate the possibility of using the extracted topics as a prior for inferring the sentiment orientation of a particular comment.