III: Small: Better Sentiment Analysis Through Forecasting

The emerging field of sentiment analysis employs algorithmic methods to identify and summarize opinions expressed in text. Both machine learning and ad-hoc approaches lie at the foundations of contemporary sentiment analysis systems, but progress on improving both precision and recall has been slowed by the expense and complexity of obtaining sufficiently broad, general sentiment training/validation data. Recent work has established that fundamental economic variables can successfully be forecast by applying sentiment analysis methods to news-oriented text streams. This project turns this relation on its head, using such forecasting approaches to improve both the precision and recall of general entity-oriented sentiment analysis methods. In particular, this project provides a three-pronged research effort into entity-level sentiment analysis, focusing on improved assessment and algorithms, with applications to the social sciences and forecasting. In particular: (1) Developing a complete entity-level, text and language-independent sentiment evaluation environment, both to further the development of the Lydia system and for release to the international sentiment analysis community. (2) Building on this environment, to develop improved sentiment-detection methods for English news, foreign language news streams, social media such as blogs and Twitter, and historical text corpora. (3) Finally, applying improved sentiment analysis to a variety of challenges in the social sciences. This research promises to substantially improve both the precision and recall of sentiment detection methods, by focusing on the weakest link: rigorous yet domain-, source-, and language-independent assessment of sentiment. Beyond improvements in natural language processing (NLP), this includes other issues in opinion mining, including article clustering and duplicate detection, entity-domain context, and combining opinions from large numbers of distinct sources. The sentiment analysis methods and data developed under this research project are expected to have a broad impact, as the results will be directly applicable in a broad range of social sciences, including sociology, economics, political science, and media and communication studies. The techniques will serve as both an educational and scholarly resource in these fields, empowering students and researchers to conduct their own primary studies on historical trends and social forces. Results will be disseminated to the community through the project website (http://www.textmap.org/III).