Discrete financial in sentimental analysis using exploring patterns and trends

Upreti, Kamal; Kapoor, Anmol; Tiwari, Akhilesh; Gupta, Himanshu; Kushwah, Virendra Singh; Parashar, Jyoti; Gangwar, Divya; Kumar, Nishant

doi:10.47974/JDMSC-2079

Open Access Research Article

Discrete financial in sentimental analysis using exploring patterns and trends

^*Kamal UpretiCorresponding authorkamalupreti1989@gmail.comDepartment of Computer ScienceDelhi NCR CampusCHRIST UniversityUttar Pradesh, 201003, IndiaView full profile → , Anmol Kapoorkapoor.anmol98@gmail.comDepartment of Computer Science and EngineeringMaharaja Surajmal Institute of TechnologyJanakpuri, New Delhi, 110058, IndiaView full profile → , Akhilesh Tiwariakhileshmatiwari1@gmail.comSchool of Business and ManagementDelhi NCR CampusCHRIST UniversityUttar Pradesh, 201003, IndiaView full profile → , Himanshu Guptahimanshugpt13@gmail.comDepartment of Computer EngineeringSyracuse UniversityWoodridge, New Jersey, 13244, United StatesView full profile → , Virendra Singh Kushwahkushwah.virendra248@gmail.comSchool of Computer Science & EngineeringBhopal-Indore HighwayVIT Bhopal UniversityBhopal, Madhya Pradesh, 466114, IndiaView full profile → , Jyoti Parasharjyoti.parashar123@gmail.comDepartment of Computer Science and EngineeringDr. Akhilesh Das Gupta Institute of Professional StudiesShashti Park, New Delhi, 110053, IndiaView full profile → , Divya Gangwardivyagangwar2013@gmail.comDepartment of ManagementBabu Banarasi Das Institute of Technology and ManagementDr. A.P.J Abdul Kalam Technical UniversityUttar Pradesh, 226028, IndiaView full profile → , Nishant Kumarnishantkumar00@gmail.comSchool of Business and ManagementCHRIST UniversityBangalore, Karnataka, 560029, IndiaView full profile →

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pp. 2053–2065Vol. 27Issue 7October 2024DOI: 10.47974/JDMSC-2079 Crossmark XML

Received:: 06 Mar 2024
Published Online:: 15 Oct 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2079
Pages:: 2053–2065

Abstract

In today’s rapidly evolving financial environment, it’s 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 investor’s 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.

Keywords

Market sentiment Investment prediction Text mining Financial forecasting

Subject Classifications

Primary 68R01Secondary 97N70

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