Abstract
The spread of social networks as Twitter, Facebook, or Google+ or specialized ones as LinkedIn or Viadeo allows sharing opinions on different aspects of life every day. Millions of messages appear daily on the web thanks to blogs, microblogs, social networks, or review collector sites. This textual information can be divided in two main categories: facts and opinions. Facts are objective statements, while opinions reject and reveal people’s sentiments about products, personalities, and events and are extremely important when someone needs opinions before taking a decision. This information is a rich source of data for opinion mining. The interest that potential customers show in online opinions and reviews about products is something that vendors are gradually paying more and more attention to. In this scenario, a promising approach is sentiment analysis: the computational study of opinions, sentiments, and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this chapter, we investigate the literature’s state of the art and the adoption of a probabilistic approach based on the latent Dirichlet allocation (LDA). By this approach, for a set of documents belonging to a same knowledge domain, a graph, the mixed graph of terms (mGTs), can be automatically extracted. This graph contains a set of weighted word pairs, which are discriminative for sentiment classification. The proposed method has been applied for the real-time analysis of documents, as TripAdvisor’s posts, in Italian language of opinion holders or social groups in the Databenc context: urban spaces, museums, archaeological parks, and events.
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Chang, SK., Greco, L., De Santo, A. (2015). Sentiment Detection in Social Networks Using Semantic Analysis: A Tool for Sentiment Analysis and Its Application in Cultural Heritage Realm. In: Colace, F., De Santo, M., Moscato, V., Picariello, A., Schreiber, F., Tanca, L. (eds) Data Management in Pervasive Systems. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20062-0_9
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DOI: https://doi.org/10.1007/978-3-319-20062-0_9
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