Aspect-Based Opinion Mining Using Knowledge Bases
作者： Marco Federici , Mauro Dragoni
DOI: 10.1007/978-3-319-69146-6_13
关键词: Polarity (physics) 、 SemEval 、 Focus (computing) 、 Field (computer science) 、 Computation 、 Limiting 、 Computer science 、 Data mining 、 Information retrieval 、 Sentiment analysis 、 Task (project management)
摘要: In the last decade, focus of Opinion Mining field moved to detection pairs “aspect-polarity” instead limiting approaches in computation general polarity a text. this work, we propose an aspect-based opinion mining system based on use semantic resources for extraction aspects from text and their polarities. The proposed participated at third edition Semantic Sentiment Analysis (SSA) challenge took place during ESWC 2017 achieving runner-up Task #2 concerning sentiment analysis. Moreover, further evaluation performed SemEval 2015 benchmarks demonstrated feasibility approach.
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