Sentiment relevance detection problems occur when there is a sentiment expression in a text, and there is the question of whether or not the expression is related to a given entity or, more generally, to a given situation. The paper discusses variants of the problem, and shows that it is distinct from other somewhat similar problems occurring in the field of sentiment analysis and opinion mining. We experimentally demonstrate that using the information about relevancy significantly affects the final sentiment evaluation of the entities. We then compare a set of different algorithms for solving the relevance detection problem. The most accurate results are achieved by algorithms that use certain document-level information about the target entities. We show that this information can be accurately extracted using supervised classification methods.
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Entities’ Sentiment Relevance
Semantic Scholar · Computer Science · 2014
Abstract
Sentiment relevance detection problems occur when there is a sentiment expression in a text, and there is the question of whether or not the expression is related to a given entity or, more generally, to a given situation. The paper discusses variants of the problem, and shows that it is distinct from other somewhat similar problems occurring in the field of sentiment analysis and opinion mining. We experimentally demonstrate that using the information about relevancy significantly affects the final sentiment evaluation of the entities. We then compare a set of different algorithms for solving the relevance detection problem. The most accurate results are achieved by algorithms that use certain document-level information about the target entities. We show that this information can be accurately extracted using supervised classification methods.