Discrete financial in sentimental analysis using exploring patterns and trends
- Title
- Discrete financial in sentimental analysis using exploring patterns and trends
- Creator
- Upreti K.; Kapoor A.; Tiwari A.; Gupta H.; Kushwah V.S.; Parashar J.; Gangwar D.; Kumar N.
- Description
- In todays rapidly evolving financial environment, its crucial for investors and decision-makers to effectively analyze stakeholder communications to gain valuable insights. This research conducts a comprehensive evaluation of a range of models that utilize machine learning, such as CNN (Convolutional Neural Network), LR (Logistic Regression), Doc2vec, and LSTM (Long Short-Term Memory), to determine their efficacy in interpreting investors sentiments and predicting business assessments and trading dynamics. The justification for preferring deep neural architectures compared to conventional data analysis lies in the challenge of handling extensive amounts of diverse and unorganized data. Deep learning techniques have shown impressive capacity in automatically detecting complex characteristics and unveiling concealed patterns within written records, rendering them well-suited for sentiment analysis in financial dialogue. This research questions the notion that depending exclusively on data from a solitary origin leads to persistently effective investment moves. In fact, stakeholder communication is impacted by numerous influential elements, leading to diverse sentiments and sentiments. Through our comparative assessment, we aim to illuminate how various deep learning models can adeptly capture the intricate nuances of sentiment within fiscal messaging. 2024, Taru Publications. All rights reserved.
- Source
- Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 7, pp. 2053-2065.
- Date
- 2024-01-01
- Publisher
- Taru Publications
- Subject
- Financial forecasting; Investment prediction; Market sentiment; Text mining
- Coverage
- Upreti K., Department of Computer Science, CHRIST University, Delhi NCR Campus Uttar Pradesh, 201003, India; Kapoor A., Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, 110058, India; Tiwari A., School of Business and Management, CHRIST University, Delhi NCR Campus Uttar Pradesh, 201003, India; Gupta H., Department of Computer Engineering, Syracuse University, Woodridge, 13244, NJ, United States; Kushwah V.S., School of Computer Science & Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, 466114, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Shashti Park, New Delhi, 110053, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology and Management, Dr. A.P.J Abdul Kalam Technical University, Uttar Pradesh, 226028, India; Kumar N., School of Business and Management, CHRIST University, Karnataka, Bangalore, 560029, India
- Rights
- Restricted Access
- Relation
- ISSN: 9720529
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Upreti K.; Kapoor A.; Tiwari A.; Gupta H.; Kushwah V.S.; Parashar J.; Gangwar D.; Kumar N., “Discrete financial in sentimental analysis using exploring patterns and trends,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/13404.