Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Ahmad, Hassan; Umar, Shahla U.

doi:10.31449/inf.v47i5.4673

Cited by 3 publications

(1 citation statement)

References 22 publications

(22 reference statements)

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“…The constructed machine learning model used ultinomial Naive Bayes (MNB) and logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) [2], [3]. The challenge of feature selection in text categorization is a significant one.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for text document classification-efficient classification approach

Mohammed Ali,

Nihad,

Mohamed Sharaf

et al. 2024

IJ-AI

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<p>Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.</p>

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Section: Introductionmentioning

confidence: 99%

Machine learning for text document classification-efficient classification approach

Mohammed Ali,

Nihad,

Mohamed Sharaf

et al. 2024

IJ-AI

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Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

Alshahrani,

Hassan,

Almalki

et al. 2023

IEEE Access

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Natural Language Processing (NLP) is the most vital technology in currently utilized, specifically caused by the huge and growing count of online texts that requires that understood for its massive value that completely asserted. NLP is create a sense of unstructured data, which are created by social networks and other social data sources, and is supported to organize them into an additional structured model that assists several kinds of tasks and applications. Sentiment analysis (SA), a subfield of NLP contains determining the sentiment expressed or emotional tone from a piece of text. Deep learning (DL) approaches are significantly advanced the field of SA, permitting for more accurate and nuanced classification of sentiments from the text data. In this article, we present an Advanced Sentiment Analysis using a Red-Tailed Hawk Optimizer with Ensemble Learning (ASA-RTHEL) Strategy in NLP. The aim of ASA-RTHEL technique is to exploit the strategies of ensemble learning with a hyperparameter tuning process for SA. The ASA-RTHEL technique mainly follows an ensemble learning-based classification process, which combines prediction from three DL approaches convolutional neural network (CNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The ensemble process results in enhanced SA performance and decreases the risk of depending only on a single model bias or error. To boost the SA performance, the hyperparameter tuning strategy is performed by the use of the RTH algorithm. An extensive set of experiments were carried out for ensuring the superior SA results of ASA-RTHEL technique. The comprehensive comparison study highlighted the enhanced results of the MPONLP-TSA method on the recognition of various kinds of sentiments.INDEX TERMS Sentiment analysis; Natural language processing; Red-Tailed Hawk Optimizer; Deep learning; Ensemble learning.

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A Hybrid Neural Network Model for Sentiment Analysis of Financial Texts Using Topic Extraction, Pre-Trained Model, and Enhanced Attention Mechanism Methods

Duan,

Yan,

Zhang

2024

IEEE Access

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Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models

Cited by 3 publications

References 22 publications

Machine learning for text document classification-efficient classification approach

Machine learning for text document classification-efficient classification approach

Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

A Hybrid Neural Network Model for Sentiment Analysis of Financial Texts Using Topic Extraction, Pre-Trained Model, and Enhanced Attention Mechanism Methods

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