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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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/10356/50844 |
| _version_ | 1811683993482952704 |
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| author | Anh Cuong, Tran |
| author2 | Hoi Chu Hong |
| author_facet | Hoi Chu Hong Anh Cuong, Tran |
| author_sort | Anh Cuong, Tran |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:21:34Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/50844 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:21:34Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/508442023-03-03T20:33:09Z Finance data mining1 Anh Cuong, Tran Hoi Chu Hong School of Computer Engineering Centre for Advanced Information Systems Bin Li DRNTU::Engineering::Computer science and engineering::Data DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing 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. Bachelor of Engineering (Computer Science) 2012-11-21T08:07:34Z 2012-11-21T08:07:34Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/50844 en Nanyang Technological University 40 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Data DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing Anh Cuong, Tran Finance data mining1 |
| title | Finance data mining1 |
| title_full | Finance data mining1 |
| title_fullStr | Finance data mining1 |
| title_full_unstemmed | Finance data mining1 |
| title_short | Finance data mining1 |
| title_sort | finance data mining1 |
| topic | DRNTU::Engineering::Computer science and engineering::Data DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing |
| url | http://hdl.handle.net/10356/50844 |
| work_keys_str_mv | AT anhcuongtran financedatamining1 |