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Latent Syntactic Structure-Based Sentiment Analysis

Hangya, Viktor and Szántó, Zsolt and Farkas, Richárd (2017) Latent Syntactic Structure-Based Sentiment Analysis. In: 2nd IEEE International Conference on Computational Intelligence and Applications, 08-10 Sep 2017, Beijing, China.

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Abstract

People share their opinions about things like products, movies and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts, thus automatic sentiment analysis is a popular area of applied artificial intelligence. We propose a latent syntactic structure-based approach for sentiment analysis which requires only sentence-level polarity labels for training. Our experiments on three domains (movie, IT products, restaurant) show that a sentiment analyzer that exploits syntactic parses and has access only to sentence-level polarity annotation for in-domain sentences can outperform state-of-the-art models that were trained on out-domain parse trees with sentiment annotation for each node of the trees. In practice, millions of sentence-level polarity annotations are usually available for a particular domain thus our approach is applicable for training a sentiment analyzer for a new domain while it can exploit the syntactic structure of sentences as well.

Item Type: Conference or Workshop Item (Speech)
Subjects: T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában
Depositing User: Dr Richárd Farkas
Date Deposited: 01 Oct 2017 19:51
Last Modified: 01 Oct 2017 19:51
URI: http://real.mtak.hu/id/eprint/64698

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