Deeper sentiment analysis using machine translation technology
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Extracting Causal Knowledge Using Clue Phrases and Syntactic Patterns
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
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This paper proposes a method to automatically extract basis expressions that indicate economic trends from newspaper articles by using a statistical method. We also propose a method to classify them into positive expressions that indicate upbeat, and negative expressions that indicate downturn in economy, respectively. It is important for companies, governments and investors to predict economic trends in order to forecast revenue, sales of products, prices of commodities and stock prices. We considered that basis expressions are useful for the companies, governments and investors to forecast economic trends. We extracted basis expressions, and classified them into positive expressions or negative expressions as information to forecast economic trends. Our method used a bootstrap method that was minimally a supervised algorithm for extracting basis expressions. Moreover, our method classified basis expressions into positive expressions or negative ones without dictionaries.