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.

Preview

Text paper-iccai.pdf Download (835kB) | Preview

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.

Actions (login required)

Edit Item