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

This paper describes the details of the system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter, which used a linear classifier trained by stochastic gradient descent with hinge loss and elastic net regularization to make predictions.

Abstract

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 techniques and task-specific feature engineering. We used a linear classifier trained by stochastic gradient descent with hinge loss and elastic 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. 1