Semantic Sentiment Analysis Challenge at ESWC2018
作者： Mauro Dragoni , Erik Cambria
DOI: 10.1007/978-3-030-00072-1_10
关键词:
摘要: Sentiment Analysis is a widely studied research field in both and industry, there are different approaches for addressing sentiment analysis related tasks. engines implement spanning from lexicon-based techniques, to machine learning, or involving syntactical rules analysis. Such systems already evaluated international challenges. However, Semantic approaches, which take into account rely also on large semantic knowledge bases Web best practices, not under specific experimental evaluation comparison by other may potentially deliver higher performance, since they able analyze the implicit, semantics features associated with natural language concepts. In this paper, we present fifth edition of Challenge, implementing relying competition test sets, Systems merely based syntax/word-count just have been excluded evaluation. Then, results each task.
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sci-hub.se 参考文章(11)
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