Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis
Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its sto...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/19/1/7 |
| _version_ | 1827305860484825088 |
|---|---|
| author | Bassant A. Abdelfattah Saad M. Darwish Saleh M. Elkaffas |
| author_facet | Bassant A. Abdelfattah Saad M. Darwish Saleh M. Elkaffas |
| author_sort | Bassant A. Abdelfattah |
| collection | DOAJ |
| description | Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset. |
| first_indexed | 2024-04-24T18:06:09Z |
| format | Article |
| id | doaj.art-97349f304ce04cc1a077c865076cf884 |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-04-24T18:06:09Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-97349f304ce04cc1a077c865076cf8842024-03-27T13:50:19ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762024-01-0119111613410.3390/jtaer19010007Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment AnalysisBassant A. Abdelfattah0Saad M. Darwish1Saleh M. Elkaffas2Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby, Alexandria 21526, EgyptDepartment of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El-Shatby, Alexandria 21526, EgyptDepartment of Information Systems, Arab Academy for Science and Technology, Alexandria 1029, EgyptSocial media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.https://www.mdpi.com/0718-1876/19/1/7long short-term memoryneutrosophic logicsentiment analysis |
| spellingShingle | Bassant A. Abdelfattah Saad M. Darwish Saleh M. Elkaffas Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis Journal of Theoretical and Applied Electronic Commerce Research long short-term memory neutrosophic logic sentiment analysis |
| title | Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis |
| title_full | Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis |
| title_fullStr | Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis |
| title_full_unstemmed | Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis |
| title_short | Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis |
| title_sort | enhancing the prediction of stock market movement using neutrosophic logic based sentiment analysis |
| topic | long short-term memory neutrosophic logic sentiment analysis |
| url | https://www.mdpi.com/0718-1876/19/1/7 |
| work_keys_str_mv | AT bassantaabdelfattah enhancingthepredictionofstockmarketmovementusingneutrosophiclogicbasedsentimentanalysis AT saadmdarwish enhancingthepredictionofstockmarketmovementusingneutrosophiclogicbasedsentimentanalysis AT salehmelkaffas enhancingthepredictionofstockmarketmovementusingneutrosophiclogicbasedsentimentanalysis |