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Recent Advances in Computer Science and Communications


ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions based on Sentiment Analysis

Author(s): K. Kalaiselvi* and Vasantha Kalyani David

Volume 16, Issue 8, 2023

Published on: 18 September, 2023

Article ID: e150823219709 Pages: 11

DOI: 10.2174/2666255816666230815121119

Price: $65


Background: A significant problem in economics is stock market prediction. Due to the noise and volatility, however, timely prediction is typically regarded as one of the most difficult challenges. A sentiment-based stock price prediction that takes investors' emotional trends into account to overcome these difficulties is essential.

Objective: This study aims to enhance the ELM's generalization performance and prediction accuracy.

Methods: This article presents a new sentiment analysis based-stock prediction method using a modified extreme learning machine (ELM) with deterministic weight modification (DWM) called S-DELM. First, investor sentiment is used in stock prediction, which can considerably increase the model's predictive power. Hence, a convolutional neural network (CNN) is used to classify the user comments. Second, DWM is applied to optimize the weights and biases of ELM.

Results: The results of the experiments demonstrate that the S-DELM may not only increase prediction accuracy but also shorten prediction time, and investors' emotional tendencies are proven to help them achieve the expected results.

Conclusion: The performance of S-DELM is compared with different variants of ELM and some conventional method.

Keywords: Stock market prediction, Convolutional neural network, extreme learning machine, sentiment analysis, deterministic weight modification, machine learning.

Graphical Abstract
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