SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

TL;DR

This paper introduces SlangSD, a comprehensive sentiment dictionary for slang words, to improve sentiment analysis of social media content, especially short and informal texts, by leveraging web resources.

Contribution

It presents the first slang sentiment dictionary built from web resources, enhancing sentiment classification in social media analysis.

Findings

SlangSD improves sentiment classification accuracy.

SlangSD is publicly available for research.

The dictionary is easy to extend and maintain.

Abstract

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.