Humboldt-Universität zu Berlin

Computational Analysis of Text Sentiment

PI: Project Supervisor: Dr. Maite Taboada; September 2009 – December 2012; Simon Fraser University

The goal of this project is to develop a computational system for automatically extracting sentiment from any given text. Sentiment is characterized as positive or negative views expressed by the subjective content of a text (e.g., an opinion piece in a newspaper or a movie review). We hypothesize that, given a text, we can determine whether it contains sentiment or subjective content, and if it does, we can also determine the type of the sentiment – categorically positive or negative, based on the analysis of the discourse structure of the text. In this project, my contributions were related to developing resources for discourse parsing. Specifically, I conducted a corpus study in order to extract relevant linguistic signals (e.g., discourse markers) of coherence relations, and then formulated rules for identifying coherence relations in unseen texts based on the contextual information about the occurrence of those signals.