Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns

Abstract

Social networking sites have flooded the Internet with posts containing shared opinions, moods, and feelings. This has given rise to a new wave of research to develop algorithms for emotion detection and extraction on social data. As the desire to understand how people feel about certain events/objects across countries or regions grows, the need to analyze social data in different languages grows with it. However, the explosive nature of data generated around the world brings a challenge for sentiment-based information retrieval and analysis. In this paper, we propose a multilingual system with a computationally inexpensive approach to sentiment analysis of social data. The experiments demonstrate that our approach performs an effective multi-lingual sentiment analysis of microblog data with little more than a 100 emotion-bearing patterns.