Coarse- and Fine-Grained Sentiment Analysis of Social Media Text

Sentiment analysis-the automated extraction of expressions of positive or negative attitudes from text-has received considerable attention from researchers during the past iO years. During the same period, the widespread growth of social media has resulted in an explosion of publicly available, user-generated text on the World Wide Web. These data can potentially be utilized to provide real-time insights into the aggregated sentiments of people. The tools provided by statistical natural language processing and machine learning, along with exciting new scalable approaches to working with large volumes of text, make it possible to begin extracting sentiments from the web. We discuss some of the challenges of sentiment extraction and some of the approaches employed to address these challenges. In particular, we describe work we have done to annotate sentiment in blogs at the levels of sentences and subsentences (clauses); to classify subjectivity at the level of sentences; and to identify the targets, or topics, of sentiment at the level of clauses.