DWWP: Domain-specific new words detection and word propagation system for sentiment analysis in the tourism domain

By Wei Li, Wei Li, Kun Guo, Yong Shi, Luyao Zhu, Yuanchun Zheng. 2018

45 citations recorded by the source corpus.

Abstract Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection (DW) and word propagation (WP) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on Dataset I and Dataset II, respectively.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Kyungbok Min
2. Dang Lien Minh
3. Hyeonjoon Moon

Citations in the corpus

• Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, methodology): “Li et al. [13] proposed a system which can be applied to hot scenic spots analysis and precision marketing by applying sentiment analysis on customer’s reviews from Qunar.com and Ctrip.com.”