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

Carlos Argueta, Yi-Shin Chen
2014 Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)  
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
more » ... timent-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. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details:
doi:10.3115/v1/w14-5906 dblp:conf/acl-socialnlp/ArguetaC14 fatcat:qijt4ywjmnarhaal46dzp2praq