Paper: Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

ACL ID D11-1014
Title Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We introduce a novel machine learning frame- work based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment prediction tasks these represen- tations outperform other state-of-the-art ap- proaches on commonly used datasets, such as movie reviews, without using any pre-defined sentiment lexica or polarity shifting rules. We also evaluate the model’s ability to predict sentiment distributions on a new dataset based on confessions from the experience project. The dataset consists of personal user stories annotated with multiple labels which, when aggregated, form a multinomial distribution that captures emotional reactions. Our al- gorithm can more accurately predict dist...