Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation

TL;DR

This paper introduces an automatic framework for extracting domain-specific sentiment words by expanding seeds with synonyms, generating patterns through graph propagation, and automatically capturing syntactic relations, improving over manual pattern methods.

Contribution

The paper proposes a novel automatic approach combining seed expansion, synonymy graph propagation, and pattern generation for domain-specific sentiment word extraction.

Findings

Effective in three domains

Outperforms manual pattern methods

Automates large-scale pattern detection

Abstract

This paper focuses on the automatic extraction of domain-specific sentiment word (DSSW), which is a fundamental subtask of sentiment analysis. Most previous work utilizes manual patterns for this task. However, the performance of those methods highly relies on the labelled patterns or selected seeds. In order to overcome the above problem, this paper presents an automatic framework to detect large-scale domain-specific patterns for DSSW extraction. To this end, sentiment seeds are extracted from massive dataset of user comments. Subsequently, these sentiment seeds are expanded by synonyms using a bootstrapping mechanism. Simultaneously, a synonymy graph is built and the graph propagation algorithm is applied on the built synonymy graph. Afterwards, syntactic and sequential relations between target words and high-ranked sentiment words are extracted automatically to construct large-scale patterns, which are further used to extracte DSSWs. The experimental results in three domains reveal the effectiveness of our method.