Abstract

Diarmuid will be covering the following:

Richard Socher; Jeffrey Pennington; Eric H. Huang; Andrew Y. Ng; Christopher D. Manning Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

http://aclweb.org/anthology-new/D/D11/D11-1014.pdf

We introduce a novel machine learning framework 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 representations outperform other state-of-the-art approaches 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 algorithm can more accurately predict distributions over such labels compared to several competitive baselines.