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Predictive Analytics for Stock Prices using Sentiment Analysis

by Salma Elsayed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 48
Year of Publication: 2022
Authors: Salma Elsayed
10.5120/ijca2022921888

Salma Elsayed . Predictive Analytics for Stock Prices using Sentiment Analysis. International Journal of Computer Applications. 183, 48 ( Jan 2022), 32-37. DOI=10.5120/ijca2022921888

@article{ 10.5120/ijca2022921888,
author = { Salma Elsayed },
title = { Predictive Analytics for Stock Prices using Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2022 },
volume = { 183 },
number = { 48 },
month = { Jan },
year = { 2022 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number48/32256-2022921888/ },
doi = { 10.5120/ijca2022921888 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:17.597982+05:30
%A Salma Elsayed
%T Predictive Analytics for Stock Prices using Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 48
%P 32-37
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Stock prediction is consider one of the most popular tasks in the last decade. Stock prediction can be achieved through the analysis of the numerical values (e.g., open price and close price) or the sentiment analysis of social media text (e.g., tweets). In this paper, we will discuss the several approach of stock prediction using sentiment analysis methods. These methods can be classified into four main categories, namely, machine learning, lexicon, graph, and hybrid based methods. Besides, we discussed the basic tools used to help in the task of sentiment analysis such as Term Frequency Inverse Document Frequency and word2cev algorithms.

References
  1. Jin, Z., Y. Yang, and Y. Liu, Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 2019: p. 1-17.
  2. Mohan, S., et al. Stock price prediction using news sentiment analysis. in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService). 2019. IEEE.
  3. Nemes, L. and A. Kiss, Prediction of stock values changes using sentiment analysis of stock news headlines. Journal of Information and Telecommunication, 2021: p. 1-20.
  4. Yue, L., et al., A survey of sentiment analysis in social media. Knowledge and Information Systems, 2019. 60(2): p. 617-663.
  5. Feldman, R., Techniques and applications for sentiment analysis. Communications of the ACM, 2013. 56(4): p. 82-89.
  6. Mukherjee, S., Sentiment analysis, in ML. NET Revealed. 2021, Springer. p. 113-127.
  7. Gopal, S. and M. Ramasamy, Hybrid multiple structural break model for stock price trend prediction. The Spanish Review of Financial Economics, 2017. 15(2): p. 41-51.
  8. Patil, P., et al. Stock market prediction using ensemble of graph theory, machine learning and deep learning models. in Proceedings of the 3rd International Conference on Software Engineering and Information Management. 2020.
  9. Haddi, E., X. Liu, and Y. Shi, The role of text pre-processing in sentiment analysis. Procedia Computer Science, 2013. 17: p. 26-32.
  10. Xu, G., et al., Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 2019. 7: p. 51522-51532.
  11. Tang, D., et al. Learning sentiment-specific word embedding for twitter sentiment classification. in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014.
  12. Nti, I.K., A.F. Adekoya, and B.A. Weyori, Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana. Appl. Comput. Syst., 2020. 25(1): p. 33-42.
  13. Yadav, A. and D.K. Vishwakarma, Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 2020. 53(6): p. 4335-4385.
  14. Kumar, C.P. and L.D. Babu, Novel text preprocessing framework for sentiment analysis, in Smart Intelligent Computing and Applications. 2019, Springer. p. 309-317.
  15. Avinash, M. and E. Sivasankar, A study of feature extraction techniques for sentiment analysis, in Emerging Technologies in Data Mining and Information Security. 2019, Springer. p. 475-486.
  16. Duong, H.-T. and T.-A. Nguyen-Thi, A review: preprocessing techniques and data augmentation for sentiment analysis. Computational Social Networks, 2021. 8(1): p. 1-16.
  17. Chiong, R., et al. A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. in Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2018.
  18. Porshnev, A., I. Redkin, and A. Shevchenko. Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. in 2013 IEEE 13th International Conference on Data Mining Workshops. 2013. IEEE.
  19. Kordonis, J., S. Symeonidis, and A. Arampatzis. Stock price forecasting via sentiment analysis on Twitter. in Proceedings of the 20th Pan-Hellenic Conference on Informatics. 2016.
  20. Wu, D.D., L. Zheng, and D.L. Olson, A decision support approach for online stock forum sentiment analysis. IEEE transactions on systems, man, and cybernetics: systems, 2014. 44(8): p. 1077-1087.
  21. Bourezk, H., et al. Analyzing Moroccan Stock Market using Machine Learning and Sentiment Analysis. in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). 2020. IEEE.
  22. Sohangir, S., N. Petty, and D. Wang. Financial sentiment lexicon analysis. in 2018 IEEE 12th international conference on semantic computing (ICSC). 2018. IEEE.
  23. Zhao, B., et al. Stock market prediction exploiting microblog sentiment analysis. in 2016 International Joint Conference on Neural Networks (IJCNN). 2016. IEEE.
  24. Sakphoowadon, S., N. Wisitpongphan, and C. Haruechaiyasak. Probabilistic lexicon-based approach for stock market prediction: A case study of the Stock Exchange of Thailand (SET). in 2018 18th International symposium on communications and information technologies (ISCIT). 2018. IEEE.
  25. Turner, Z., K. Labille, and S. Gauch, Lexicon-based sentiment analysis for stock movement prediction. Journal of Construction Materials, 2021. 2: p. 3-5.
  26. Yoon, B., Y. Jeong, and S. Kim, Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning. IEEE Access, 2020. 8: p. 161943-161957.
  27. Kia, A.N., S. Haratizadeh, and S.B. Shouraki, A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices. Expert Systems with Applications, 2018. 105: p. 159-173.
  28. Wankhade, S.B., et al., Hybrid model based on unification of technical analysis and sentiment analysis for stock price prediction. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013. 11(9): p. 3025-3033.
  29. Hsu, C.-M., A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Systems with Applications, 2011. 38(11): p. 14026-14036.
  30. Panday, H., et al., Stock Prediction using Sentiment analysis and Long Short Term Memory. European Journal of Molecular & Clinical Medicine, 2020. 7(2): p. 5060-5069.
  31. Yujun, Y., Y. Yimei, and X. Jianhua, A hybrid prediction method for stock price using LSTM and ensemble EMD. Complexity, 2020. 2020.
  32. Fathalla, A., et al., Deep end-to-end learning for price prediction of second-hand items. Knowledge and Information Systems, 2020. 62(12): p. 4541-4568.
  33. Hosny, K.M., et al., Fast computation of 2D and 3D Legendre moments using multi-core CPUs and GPU parallel architectures. Journal of Real-Time Image Processing, 2019. 16(6): p. 2027-2041.
  34. Salah, A., K. Li, and K. Li, Lazy-Merge: A Novel Implementation for Indexed Parallel $ K $-Way In-Place Merging. IEEE Transactions on Parallel and Distributed Systems, 2015. 27(7): p. 2049-2061.
  35. Salah, A. and K. Li, PAR‐3D‐BLAST: A parallel tool for searching and aligning protein structures. Concurrency and Computation: Practice and Experience, 2014. 26(10): p. 1705-1714.
  36. Al-Moalmi, A., et al., A whale optimization system for energy-efficient container placement in data centers. Expert Systems with Applications, 2021. 164: p. 113719.
Index Terms

Computer Science
Information Sciences

Keywords

Stock price stock market sentiment analysis stock movement sentiment classification machine learning graph based lexicon based hybrid.

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