Lingosent — a platform for linguistic aware sentiment analysis for social media messages

Abstract

Sentiment analysis is an important natural language processing (NLP) task and applied to a wide range of scenarios. Social media messages such as tweets often differ from formal writing, exhibiting unorthodox capitalization, expressive lengthenings, Internet slang, etc. While such characteristics are inherently beneficial for the task of sentiment analysis, they also pose new challenges for existing NLP platforms. In this article, we present a new approach to improve lexicon-based sentiment analysis by extracting and utilizing linguistic features in a comprehensive manner. In contrast to existing solutions, we design our sentiment analysis approach as a framework with data preprocessing, linguistic feature extraction and sentiment calculation being separate components. This allows for easy modification and extension of each component. More importantly, we can easily configure the sentiment calculation with respect to the extracted features to optimize sentiment analysis for different application contexts. In a comprehensive evaluation, we show that our system outperforms existing state-of-the-art lexicon-based sentiment analysis solutions.