Paper: ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques

ACL ID S13-2074
Title ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper describes University of Leipzig?s approach to SemEval-2013 task 2B on Sen- timent Analysis in Twitter: message polar- ity classification. Our system is designed to function as a baseline, to see what we can accomplish with well-understood and purely data-driven lexical features, simple general- izations as well as standard machine learning techniques: We use one-against-one Support Vector Machines with asymmetric cost fac- tors and linear ?kernels? as classifiers, word uni- and bigrams as features and additionally model negation of word uni- and bigrams in word n-gram feature space. We consider gen- eralizations of URLs, user names, hash tags, repeated characters and expressions of laugh- ter. Our method ranks 23 out of all 48 partic- ipating systems, achieving an averaged (pos-...