Paper: Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

ACL ID D14-1052
Title Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

This paper introduces a model of multiple- instance learning applied to the predic- tion of aspect ratings or judgments of specific properties of an item from user- contributed texts such as product reviews. Each variable-length text is represented by several independent feature vectors; one word vector per sentence or paragraph. For learning from texts with known as- pect ratings, the model performs multiple- instance regression (MIR) and assigns im- portance weights to each of the sentences or paragraphs of a text, uncovering their contribution to the aspect ratings. Next, the model is used to predict aspect ratings in previously unseen texts, demonstrating interpretability and explanatory power for its predictions. We evaluate the model on seven multi-aspect sentiment analysis data sets...