Paper: GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent

ACL ID S13-2054
Title GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper describes the details of our system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter. Our approach to predicting the sentiment of Tweets and SMS is based on supervised machine learning tech- niques and task-specific feature engineering. We used a linear classifier trained by stochas- tic gradient descent with hinge loss and elas- tic net regularization to make our predictions, which were ranked first or second in three of the four experimental conditions of the shared task. Furthermore, our system makes use of social media specific text preprocessing and linguistically motivated features, such as word stems, word clusters and negation handling.