The wide spread of the social media has given users a means to express and share their opinions and thoughts on a large range of topics and events. The number of opinions, emotions, sentiments that are being expressed within social media grows at an exponential rate; all these data can be exploited in order to come up with useful insights, analytics, etc. Initial Sentiment Analysis systems used lexical and statistical resources to automatically assess polarities of opinions and sentiment. With the raise of the Semantic Web, it has been proved that Sentiment Analysis techniques can have higher performances if they use semantic features. This generated further opportunities for the research domain as well as the market domain where key stakeholders need to catch up with the latest technology if they want to be compelling. Therefore, deep understanding of natural language text and the related semantics are urgent matter to be familiar with. Following the first two editions, the third edition of the Fine-Grained Sentiment Analysis challenge aims at providing a stimulus toward this direction. On the one hand, it represents an event where researchers can learn and share their methods and how they employed Semantics for Sentiment Analysis. On the other hand, it offers an occasion for stakeholders to get an idea of what research is being developed and where the research is headed to plan future strategies within the domain of Sentiment Analysis.
|Titolo:||Challenge on fine-grained sentiment analysis within ESWC2016|
|Data di pubblicazione:||2016|
|Tipologia:||2.1 Contributo in volume (Capitolo o Saggio)|