Semantic Aspects in Sentiment Analysis

Semantics plays an important role in the accurate analysis of the context of a sentiment expression. We analyze this role from two perspectives: the way semantics is encoded in sentiment resources, such as lexica, corpora, and ontologies, and the way it is used by automatic systems that perform sentiment analysis on social media data. Specifically, we review and discuss state-of-the-art methods and tools that rely on semantic models and resources, possibly enabling reasoning, so as to deal with some key challenges for sentiment analysis. Examples of such challenges are context dependency and finer-grained sentiment detection, which can translate into the assigning of sentiment values to aspects of objects or into the design and use of a wider spectrum of affective labels, or the use of current techniques in finer-grained semantic processing. In our dealing with semantics, lexical aspects must be combined with pragmatic and cognitive issues, and other dimensions involved in conveying sentiment, such as emotions.