Topic-dependent sentiment analysis of financial blogs
O'Hare, Neil and Davy, Michael and Bermingham, Adam and Ferguson, Paul and Sheridan, Páraic and Gurrin, Cathal and Smeaton, Alan F. (2009) Topic-dependent sentiment analysis of financial blogs. In: TSA 2009 - 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, 6 November 2009, Hong Kong, China. ISBN 978-1-60558-805-6
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While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches.
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