Finance data mining1

The author uses quantitative media data generated by large-scale natural language processing (NLP) text analysis systems to perform a comprehensive and comparative study on how a Twitter mood reflects its stock trading volumes. The resources for analyzing are user profile and tweets information whic...

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Bibliographic Details
Main Author: Anh Cuong, Tran
Other Authors: Hoi Chu Hong
Format: Final Year Project
Language:English
Published: 2012
Subjects:
Online Access:http://hdl.handle.net/10356/50844
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Institution: Nanyang Technological University
Language: English
Description
Summary:The author uses quantitative media data generated by large-scale natural language processing (NLP) text analysis systems to perform a comprehensive and comparative study on how a Twitter mood reflects its stock trading volumes. The resources for analyzing are user profile and tweets information which were collected from Twitter. Even the general results for a period of 1 year are not high (10.4%), however in some specific periods such as June 2011 or from 11th September to 21st October 2011, the accuracy outcome are about 34% and 40% respectively. This is the concrete evidence to show that media data is highly informative. Besides, while building on the findings, the author realized the gaps between today sentiment analysis tools and data related to social life, which consists of many slang, common misspellings and emotions. This might affect to the general result over one year period. In general, with a well sentiment analysis tools, predicting stock market from social media data (tweets) is possible. Improvement and recommendations for future works are also discussed.