Abstract
We argue in this paper that in order to properly capture opinion and sentiment expressed in texts or dialogs any system needs a deep linguistic processing approach. As in other systems, we used ontology matching and concept search, based on standard lexical resources, but a natural language understanding system is still required to spot fundamental and pervasive linguistic phenomena. We implemented these additions to VENSES system and the results of the evaluation are compared to those reported in the state-of-the-art systems in sentiment analysis and opinion mining. We also provide a critical review of the current benchmark datasets as we realized that very often sentiment and opinion is not properly modeled.
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Delmonte, R., Pallotta, V. (2011). Opinion Mining and Sentiment Analysis Need Text Understanding. In: Pallotta, V., Soro, A., Vargiu, E. (eds) Advances in Distributed Agent-Based Retrieval Tools. Studies in Computational Intelligence, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21384-7_6
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DOI: https://doi.org/10.1007/978-3-642-21384-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21383-0
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