Sentiment analysis on social media for stock movement prediction
•A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods were proposed to capture the topic-sentiment feature.•Integration of the sentiments was investigated via a large scale experiment.•Our model o...
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| Vydáno v: | Expert systems with applications Ročník 42; číslo 24; s. 9603 - 9611 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
30.12.2015
Elsevier |
| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
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| Abstract | •A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods were proposed to capture the topic-sentiment feature.•Integration of the sentiments was investigated via a large scale experiment.•Our model outperformed other methods in the average accuracy of 18 stocks.
The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07% better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83% better accuracy than historical price method, and 3.03% better than human sentiment method. |
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| AbstractList | •A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods were proposed to capture the topic-sentiment feature.•Integration of the sentiments was investigated via a large scale experiment.•Our model outperformed other methods in the average accuracy of 18 stocks.
The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07% better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83% better accuracy than historical price method, and 3.03% better than human sentiment method. The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07% better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83% better accuracy than historical price method, and 3.03% better than human sentiment method. |
| Author | Nguyen, Thien Hai Shirai, Kiyoaki Velcin, Julien |
| Author_xml | – sequence: 1 givenname: Thien Hai surname: Nguyen fullname: Nguyen, Thien Hai email: nhthien8x@gmail.com, nhthien@jaist.ac.jp organization: School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan – sequence: 2 givenname: Kiyoaki surname: Shirai fullname: Shirai, Kiyoaki email: kshirai@jaist.ac.jp organization: School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan – sequence: 3 givenname: Julien surname: Velcin fullname: Velcin, Julien email: julien.velcin@univ-lyon2.fr organization: University of Lyon (ERIC, Lyon 2), 5 Avenue Pierre Mendes-France, 69676 Bron Cedex, France |
| BackLink | https://hal.science/hal-01203094$$DView record in HAL |
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| Cites_doi | 10.5539/emr.v1n2p46 10.1561/1500000011 10.1016/j.ipm.2009.05.001 10.1145/1462198.1462204 10.1016/j.eswa.2011.12.035 10.1016/j.eswa.2015.03.017 10.1080/07421222.2001.11045659 10.2469/faj.v57.n3.2449 10.1007/s10489-006-0001-7 10.1016/j.sbspro.2011.10.562 10.1016/j.jocs.2010.12.007 10.1016/j.eswa.2014.10.031 10.1016/j.eswa.2014.06.009 10.1111/j.1540-6261.2004.00662.x 10.1016/j.eswa.2013.04.013 10.3233/AF-13025 10.1111/j.1540-6261.1991.tb04636.x 10.1016/j.eswa.2014.07.040 10.2307/2525569 |
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| References | Patel, Shah, Thakkar, Kotecha (bib0019) 2015; 42 Tsibouris, Zeidenberg (bib0026) 1995 Zuo, Kita (bib0033) 2012; 39 Lin, He (bib0012) 2009 Schumaker, Chen (bib0022) 2009; 45 Ticknor (bib0025) 2013; 40 Walczak (bib0029) 2001; 17 Dermouche, Kouas, Velcin, Loudcher (bib0006) 2015 Si, Mukherjee, Liu, Li, Li, Deng (bib0024) 2013 Cervelló-Royo, Guijarro, Michniuk (bib0005) 2015; 42 Joachims (bib0010) 1998 Tumarkin, Whitelaw (bib0027) 2001; 57 Qian, Rasheed (bib0020) 2007; 26 Baccianella, Esuli, Sebastiani (bib0002) 2010; vol. 10 Antweiler, Frank (bib0001) 2004; 59 Patel, Shah, Thakkar, Kotecha (bib0018) 2015; 42 Fama, Fisher, Jensen, Roll (bib0008) 1969; 10 Nassirtoussi, Aghabozorgi, Wah, Ngo (bib0015) 2014; 41 Nguyen, Shirai (bib0016) 2013 Vu, Chang, Ha, Collier (bib0028) 2012 Xie, Passonneau, Wu, Creamer (bib0030) 2013 Blei, Ng, Jordan (bib0003) 2003; 3 Liu, Zhang (bib0013) 2012 Rechenthin, Street, Srinivasan (bib0021) 2013; 2 Lakkaraju, Bhattacharyya, Bhattacharya, Merugu (bib0011) 2011 Pang, Lee (bib0017) 2008; 2 Zhao, Jiang, Yan, Li (bib0032) 2010 Manning, Surdeanu, Bauer, Finkel, Bethard, McClosky (bib0014) 2014 Zhang, Fuehres, Gloor (bib0031) 2011; 26 Zuo, Kita (bib0034) 2012; 1 Bollen, Mao, Zeng (bib0004) 2011; 2 Jo, Oh (bib0009) 2011 Fama (bib0007) 1991; 46 Schumaker, Chen (bib0023) 2009; 27 Schumaker (10.1016/j.eswa.2015.07.052_bib0022) 2009; 45 Fama (10.1016/j.eswa.2015.07.052_bib0008) 1969; 10 Zuo (10.1016/j.eswa.2015.07.052_bib0033) 2012; 39 Antweiler (10.1016/j.eswa.2015.07.052_bib0001) 2004; 59 Lin (10.1016/j.eswa.2015.07.052_bib0012) 2009 Blei (10.1016/j.eswa.2015.07.052_bib0003) 2003; 3 Tumarkin (10.1016/j.eswa.2015.07.052_bib0027) 2001; 57 Cervelló-Royo (10.1016/j.eswa.2015.07.052_bib0005) 2015; 42 Joachims (10.1016/j.eswa.2015.07.052_bib0010) 1998 Walczak (10.1016/j.eswa.2015.07.052_bib0029) 2001; 17 Rechenthin (10.1016/j.eswa.2015.07.052_bib0021) 2013; 2 Patel (10.1016/j.eswa.2015.07.052_bib0018) 2015; 42 Tsibouris (10.1016/j.eswa.2015.07.052_bib0026) 1995 Manning (10.1016/j.eswa.2015.07.052_bib0014) 2014 Pang (10.1016/j.eswa.2015.07.052_bib0017) 2008; 2 Zuo (10.1016/j.eswa.2015.07.052_bib0034) 2012; 1 Lakkaraju (10.1016/j.eswa.2015.07.052_bib0011) 2011 Qian (10.1016/j.eswa.2015.07.052_bib0020) 2007; 26 Vu (10.1016/j.eswa.2015.07.052_bib0028) 2012 Jo (10.1016/j.eswa.2015.07.052_bib0009) 2011 Nguyen (10.1016/j.eswa.2015.07.052_bib0016) 2013 Nassirtoussi (10.1016/j.eswa.2015.07.052_bib0015) 2014; 41 Baccianella (10.1016/j.eswa.2015.07.052_bib0002) 2010; vol. 10 Xie (10.1016/j.eswa.2015.07.052_bib0030) 2013 Patel (10.1016/j.eswa.2015.07.052_bib0019) 2015; 42 Bollen (10.1016/j.eswa.2015.07.052_bib0004) 2011; 2 Dermouche (10.1016/j.eswa.2015.07.052_bib0006) 2015 Zhang (10.1016/j.eswa.2015.07.052_bib0031) 2011; 26 Liu (10.1016/j.eswa.2015.07.052_bib0013) 2012 Ticknor (10.1016/j.eswa.2015.07.052_bib0025) 2013; 40 Fama (10.1016/j.eswa.2015.07.052_bib0007) 1991; 46 Zhao (10.1016/j.eswa.2015.07.052_bib0032) 2010 Schumaker (10.1016/j.eswa.2015.07.052_bib0023) 2009; 27 Si (10.1016/j.eswa.2015.07.052_bib0024) 2013 |
| References_xml | – start-page: 278 year: 2013 end-page: 284 ident: bib0016 article-title: Text classification of technical papers based on text segmentation publication-title: Natural language processing and information systems - 18th international conference on applications of natural language to information systems – volume: 2 start-page: 1 year: 2008 end-page: 135 ident: bib0017 article-title: Opinion mining and sentiment analysis publication-title: Foundations and Trends in Information Retrieval – start-page: 815 year: 2011 end-page: 824 ident: bib0009 article-title: Aspect and sentiment unification model for online review analysis publication-title: Proceedings of the fourth ACM international conference on web search and data mining – start-page: 375 year: 2009 end-page: 384 ident: bib0012 article-title: Joint sentiment/topic model for sentiment analysis publication-title: Proceedings of the 18th ACM conference on information and knowledge management – year: 1998 ident: bib0010 – volume: 3 start-page: 993 year: 2003 end-page: 1022 ident: bib0003 article-title: Latent Dirichlet allocation publication-title: Journal of Machine Learning Research – start-page: 498 year: 2011 end-page: 509 ident: bib0011 article-title: Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments publication-title: Proceedings of the eleventh SIAM international conference on data mining – volume: 1 start-page: 46 year: 2012 end-page: 52 ident: bib0034 article-title: Up/down analysis of stock index by using Bayesian network publication-title: Engineering Management Research – start-page: 56 year: 2010 end-page: 65 ident: bib0032 article-title: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid publication-title: Proceedings of the 2010 conference on empirical methods in natural language processing – volume: 42 start-page: 5963 year: 2015 end-page: 5975 ident: bib0005 article-title: Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data publication-title: Expert Systems with Applications – volume: 59 start-page: 1259 year: 2004 end-page: 1294 ident: bib0001 article-title: Is all that talk just noise? The information content of internet stock message boards publication-title: The Journal of Finance – volume: 57 start-page: 41 year: 2001 end-page: 51 ident: bib0027 article-title: News or noise? Internet postings and stock prices publication-title: Financial Analysts Journal – start-page: 415 year: 2012 end-page: 463 ident: bib0013 article-title: A survey of opinion mining and sentiment analysis publication-title: Mining text data – volume: 2 start-page: 169 year: 2013 end-page: 196 ident: bib0021 article-title: Stock chatter: using stock sentiment to predict price direction publication-title: Algorithmic Finance – volume: vol. 10 start-page: 2200 year: 2010 end-page: 2204 ident: bib0002 article-title: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining publication-title: Proceedings of the seventh conference on international language resources and evaluation – volume: 42 start-page: 2162 year: 2015 end-page: 2172 ident: bib0019 article-title: Predicting stock market index using fusion of machine learning techniques publication-title: Expert Systems with Applications – volume: 26 start-page: 25 year: 2007 end-page: 33 ident: bib0020 article-title: Stock market prediction with multiple classifiers publication-title: Applied Intelligence – volume: 41 start-page: 7653 year: 2014 end-page: 7670 ident: bib0015 article-title: Text mining for market prediction: a systematic review publication-title: Expert Systems with Applications – volume: 27 start-page: 12:1 year: 2009 end-page: 12:19 ident: bib0023 article-title: Textual analysis of stock market prediction using breaking financial news: the azfin text system publication-title: ACM Transactions on Information Systems – start-page: 24 year: 2013 end-page: 29 ident: bib0024 article-title: Exploiting topic based twitter sentiment for stock prediction publication-title: Proceedings of the 51st annual meeting of the association for computational linguistics, volume 2: short papers – volume: 17 start-page: 203 year: 2001 end-page: 222 ident: bib0029 article-title: An empirical analysis of data requirements for financial forecasting with neural networks publication-title: Journal of Management Information Systems – start-page: 819 year: 2015 end-page: 824 ident: bib0006 article-title: A joint model for topic-sentiment modeling from text publication-title: ACM/SIGAPP symposium on applied computing (sac) – volume: 10 start-page: 1 year: 1969 end-page: 21 ident: bib0008 article-title: The adjustment of stock prices to new information publication-title: International Economic Review – volume: 2 start-page: 1 year: 2011 end-page: 8 ident: bib0004 article-title: Twitter mood predicts the stock market publication-title: Journal of Computational Science – volume: 26 start-page: 55 year: 2011 end-page: 62 ident: bib0031 article-title: Predicting stock market indicators through twitter “I hope it is not as bad as I fear” publication-title: Procedia - Social and Behavioral Sciences – volume: 39 start-page: 6729 year: 2012 end-page: 6737 ident: bib0033 article-title: Stock price forecast using Bayesian network publication-title: Expert Systems with Applications: An International Journal – volume: 40 start-page: 5501 year: 2013 end-page: 5506 ident: bib0025 article-title: A Bayesian regularized artificial neural network for stock market forecasting publication-title: Expert Systems with Applications – volume: 42 start-page: 259 year: 2015 end-page: 268 ident: bib0018 article-title: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques publication-title: Expert Systems with Applications – volume: 45 start-page: 571 year: 2009 end-page: 583 ident: bib0022 article-title: A quantitative stock prediction system based on financial news publication-title: Information Processing & Management – start-page: 127 year: 1995 end-page: 136 ident: bib0026 article-title: Testing the efficient markets hypothesis with gradient descent algorithms publication-title: Neural networks in the capital markets – start-page: 873 year: 2013 end-page: 883 ident: bib0030 article-title: Semantic frames to predict stock price movement publication-title: Proceedings of the 51st annual meeting of the association for computational linguistics – volume: 46 start-page: 1575 year: 1991 end-page: 1617 ident: bib0007 article-title: Efficient capital markets: II publication-title: The Journal of Finance – start-page: 55 year: 2014 end-page: 60 ident: bib0014 article-title: The Stanford CoreNLP natural language processing toolkit publication-title: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations – start-page: 23 year: 2012 end-page: 38 ident: bib0028 article-title: An experiment in integrating sentiment features for tech stock prediction in Twitter publication-title: 24th international conference on computational linguistics – volume: 1 start-page: 46 issue: 2 year: 2012 ident: 10.1016/j.eswa.2015.07.052_bib0034 article-title: Up/down analysis of stock index by using Bayesian network publication-title: Engineering Management Research doi: 10.5539/emr.v1n2p46 – volume: 2 start-page: 1 issue: 1–2 year: 2008 ident: 10.1016/j.eswa.2015.07.052_bib0017 article-title: Opinion mining and sentiment analysis publication-title: Foundations and Trends in Information Retrieval doi: 10.1561/1500000011 – volume: 45 start-page: 571 issue: 5 year: 2009 ident: 10.1016/j.eswa.2015.07.052_bib0022 article-title: A quantitative stock prediction system based on financial news publication-title: Information Processing & Management doi: 10.1016/j.ipm.2009.05.001 – start-page: 127 year: 1995 ident: 10.1016/j.eswa.2015.07.052_bib0026 article-title: Testing the efficient markets hypothesis with gradient descent algorithms – start-page: 278 year: 2013 ident: 10.1016/j.eswa.2015.07.052_bib0016 article-title: Text classification of technical papers based on text segmentation – volume: 27 start-page: 12:1 issue: 2 year: 2009 ident: 10.1016/j.eswa.2015.07.052_bib0023 article-title: Textual analysis of stock market prediction using breaking financial news: the azfin text system publication-title: ACM Transactions on Information Systems doi: 10.1145/1462198.1462204 – start-page: 24 year: 2013 ident: 10.1016/j.eswa.2015.07.052_bib0024 article-title: Exploiting topic based twitter sentiment for stock prediction – volume: 39 start-page: 6729 issue: 8 year: 2012 ident: 10.1016/j.eswa.2015.07.052_bib0033 article-title: Stock price forecast using Bayesian network publication-title: Expert Systems with Applications: An International Journal doi: 10.1016/j.eswa.2011.12.035 – start-page: 56 year: 2010 ident: 10.1016/j.eswa.2015.07.052_bib0032 article-title: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid – start-page: 498 year: 2011 ident: 10.1016/j.eswa.2015.07.052_bib0011 article-title: Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments – volume: 42 start-page: 5963 issue: 14 year: 2015 ident: 10.1016/j.eswa.2015.07.052_bib0005 article-title: Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.03.017 – start-page: 23 year: 2012 ident: 10.1016/j.eswa.2015.07.052_bib0028 article-title: An experiment in integrating sentiment features for tech stock prediction in Twitter – volume: 3 start-page: 993 year: 2003 ident: 10.1016/j.eswa.2015.07.052_bib0003 article-title: Latent Dirichlet allocation publication-title: Journal of Machine Learning Research – volume: 17 start-page: 203 issue: 4 year: 2001 ident: 10.1016/j.eswa.2015.07.052_bib0029 article-title: An empirical analysis of data requirements for financial forecasting with neural networks publication-title: Journal of Management Information Systems doi: 10.1080/07421222.2001.11045659 – year: 1998 ident: 10.1016/j.eswa.2015.07.052_bib0010 – volume: 57 start-page: 41 issue: 3 year: 2001 ident: 10.1016/j.eswa.2015.07.052_bib0027 article-title: News or noise? Internet postings and stock prices publication-title: Financial Analysts Journal doi: 10.2469/faj.v57.n3.2449 – start-page: 815 year: 2011 ident: 10.1016/j.eswa.2015.07.052_bib0009 article-title: Aspect and sentiment unification model for online review analysis – start-page: 55 year: 2014 ident: 10.1016/j.eswa.2015.07.052_bib0014 article-title: The Stanford CoreNLP natural language processing toolkit – start-page: 819 year: 2015 ident: 10.1016/j.eswa.2015.07.052_bib0006 article-title: A joint model for topic-sentiment modeling from text – volume: 26 start-page: 25 issue: 1 year: 2007 ident: 10.1016/j.eswa.2015.07.052_bib0020 article-title: Stock market prediction with multiple classifiers publication-title: Applied Intelligence doi: 10.1007/s10489-006-0001-7 – volume: 26 start-page: 55 issue: 0 year: 2011 ident: 10.1016/j.eswa.2015.07.052_bib0031 article-title: Predicting stock market indicators through twitter “I hope it is not as bad as I fear” publication-title: Procedia - Social and Behavioral Sciences doi: 10.1016/j.sbspro.2011.10.562 – volume: 2 start-page: 1 issue: 1 year: 2011 ident: 10.1016/j.eswa.2015.07.052_bib0004 article-title: Twitter mood predicts the stock market publication-title: Journal of Computational Science doi: 10.1016/j.jocs.2010.12.007 – start-page: 415 year: 2012 ident: 10.1016/j.eswa.2015.07.052_bib0013 article-title: A survey of opinion mining and sentiment analysis – start-page: 873 year: 2013 ident: 10.1016/j.eswa.2015.07.052_bib0030 article-title: Semantic frames to predict stock price movement – volume: 42 start-page: 2162 issue: 4 year: 2015 ident: 10.1016/j.eswa.2015.07.052_bib0019 article-title: Predicting stock market index using fusion of machine learning techniques publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.10.031 – volume: 41 start-page: 7653 issue: 16 year: 2014 ident: 10.1016/j.eswa.2015.07.052_bib0015 article-title: Text mining for market prediction: a systematic review publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.06.009 – volume: 59 start-page: 1259 issue: 3 year: 2004 ident: 10.1016/j.eswa.2015.07.052_bib0001 article-title: Is all that talk just noise? The information content of internet stock message boards publication-title: The Journal of Finance doi: 10.1111/j.1540-6261.2004.00662.x – volume: 40 start-page: 5501 issue: 14 year: 2013 ident: 10.1016/j.eswa.2015.07.052_bib0025 article-title: A Bayesian regularized artificial neural network for stock market forecasting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.04.013 – volume: 2 start-page: 169 issue: 3 year: 2013 ident: 10.1016/j.eswa.2015.07.052_bib0021 article-title: Stock chatter: using stock sentiment to predict price direction publication-title: Algorithmic Finance doi: 10.3233/AF-13025 – volume: 46 start-page: 1575 issue: 5 year: 1991 ident: 10.1016/j.eswa.2015.07.052_bib0007 article-title: Efficient capital markets: II publication-title: The Journal of Finance doi: 10.1111/j.1540-6261.1991.tb04636.x – volume: 42 start-page: 259 issue: 1 year: 2015 ident: 10.1016/j.eswa.2015.07.052_bib0018 article-title: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2014.07.040 – volume: vol. 10 start-page: 2200 year: 2010 ident: 10.1016/j.eswa.2015.07.052_bib0002 article-title: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining – volume: 10 start-page: 1 issue: 1 year: 1969 ident: 10.1016/j.eswa.2015.07.052_bib0008 article-title: The adjustment of stock prices to new information publication-title: International Economic Review doi: 10.2307/2525569 – start-page: 375 year: 2009 ident: 10.1016/j.eswa.2015.07.052_bib0012 article-title: Joint sentiment/topic model for sentiment analysis |
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| Snippet | •A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods... The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the... |
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| SubjectTerms | Accuracy Artificial Intelligence Classification Computer Science Construction Data mining Digital media Document and Text Processing Historic Information Retrieval Machine Learning Mathematical models Message board Opinion mining Prediction Raw materials Sentiment analysis Social media Social networks Statistics Stock Web |
| Title | Sentiment analysis on social media for stock movement prediction |
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