A consequence of the massive use of social networks, blogs, wikis, etc., is the change of users’ behaviour on, and their interaction with, the Web: opinions, emotions and sentiments are now expressed differently from the past. Lexical understanding of text is not anymore enough to detect sentiment polarities. Semantics became key for sentiment detection. This generates potential business opportunities, especially within the marketing area, and key stakeholders need to catch up with the latest technology if they want to be compelling in the market. Therefore, understanding the opinions and its peculiarities from a written text involves a deep understanding of natural language text and the semantics behind it. Recently, it has been proved that the use of semantics improves the accuracy of existing sentiment analysis systems, which are mainly based on pure machine learning or other statistical approaches. The second Edition of the Concept Level Sentiment Analysis challenge aims to provide a further stimulus in this direction by offering to researchers an event where they can learn and experiment on how to employ Semantic Web features within their sentiment analysis systems, aiming at reaching higher performance.

ESWC 2015 challenge on concept-level sentiment analysis

REFORGIATO RECUPERO, DIEGO ANGELO GAETANO;
2015

Abstract

A consequence of the massive use of social networks, blogs, wikis, etc., is the change of users’ behaviour on, and their interaction with, the Web: opinions, emotions and sentiments are now expressed differently from the past. Lexical understanding of text is not anymore enough to detect sentiment polarities. Semantics became key for sentiment detection. This generates potential business opportunities, especially within the marketing area, and key stakeholders need to catch up with the latest technology if they want to be compelling in the market. Therefore, understanding the opinions and its peculiarities from a written text involves a deep understanding of natural language text and the semantics behind it. Recently, it has been proved that the use of semantics improves the accuracy of existing sentiment analysis systems, which are mainly based on pure machine learning or other statistical approaches. The second Edition of the Concept Level Sentiment Analysis challenge aims to provide a further stimulus in this direction by offering to researchers an event where they can learn and experiment on how to employ Semantic Web features within their sentiment analysis systems, aiming at reaching higher performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/143157
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