ItemArticle

An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network

Title
An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network
Creator
Sanara, T.M.; Salma, M. Umme
Description
Sentiment analysis is one of the most widely used methods for forecasting stock market action from consumer feedback. Most of the methods associated with sentiment analysis are limited due to low accuracy and enhanced error rate. This is addressed by proposing a synergic squeeze deep info convolutional neural network-advanced variable capsule equilibrium auto encoder (SSDCNN-AVCEAE) for sentiment analysis and accurate multiple data-based stock prediction. Stock market data from NSE Nifty 50 (Mar 2, 2020May 10, 2021) and real-time twitter sentiment analysis are pre-processed through data cleaning and sentiment analyzer lexicon processes. Merging features using SSDCNN, optimized with random search algorithm, mitigates overfitting. SSDCNN eliminates redundant features. Selected features undergo classification by AVCEAE, a fusion of advanced capsule auto encoder (ACAE) and variable equilibrium optimization algorithm, enhancing prediction accuracy for rising or falling stock market movements while minimizing errors. Variable equilibrium optimization refines ACAE parameters. The proposed framework demonstrates exceptional performance with F1-Score, accuracy, false alarm rate, sensitivity, precision, specificity, and error rate reaching 98%, 99%, 0.1%, 99%, 99%, and 0.2%, respectively. The measurements highlight the model's ability to handle a variety of issues, making it a reliable option for precise stock prediction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Source
Computational Economics;Volume;67;Issue;3;pp.2077-2106
Date
01-01-2026
Publisher
Springer
Subject
AVCEAE; Sentiment analysis; Sentiment analyzer lexicon process; SSDCNN; Stock market
Coverage
Sanara T.M., Department of Computer Science, Christ (Deemed to be University) Central Campus, Karnataka, Bengaluru, 560029, India; Salma M.U., Department of Statistics and Data Science, Christ (Deemed to be University) Central Campus, Karnataka, Bengaluru, 560029, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 9277099;
Format
online
Language
English
Type
Article

Citation

Sanara, T.M.; Salma, M. Umme, “An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/21878.

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