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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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:sentiment analysis; opinion mining; financial blogs;
Subjects:Computer Science > Machine learning
Computer Science > Information storage and retrieval systems
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Published in:CIKM 2009 (The 18th ACM Conference on Information and Knowledge Management). . Association for Computing Machinery. ISBN 978-1-60558-805-6
Publisher:Association for Computing Machinery
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI 07/CE/I1147, Enterprise Ireland, EI IP/2008/0549
ID Code:14830
Deposited On:08 Sep 2009 16:12 by Neil OHare. Last Modified 02 Mar 2017 14:04

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