A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

Daniel, D. Anand Joseph; Meena, M. Janaki

doi:10.12694/scpe.v22i1.1839

Cited by 8 publications

(2 citation statements)

References 26 publications

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“…Current research topics in SA include better natural language processing (NLP) methods, data mining for predictive investigation, and text contextual comprehension [18]. It has been common practice to employ support vector machines (SVM) to address various NLP issues for quite some time.…”

Section: Motivation Of the Research Workmentioning

confidence: 99%

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Kumar,

Thatha,

Udayaraju

et al. 2024

IEEE Access

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Sentiment Analysis (SA) holds considerable significance in comprehending public perspectives and conducting precise opinion-based evaluations, making it a prominent theme in natural language processing research. With the increasing trend of online shopping and social media usage, there is a constant influx of diverse data types such as images, videos, audio, and text. Notably, text stands out as the most crucial form of unstructured data, demanding heightened attention from researchers. Given the voluminous nature of data, various methodologies have been proposed to effectively mine big datasets for valuable insights. The challenge of accurately identifying polarity in extensive customer evaluations persists due to the intricacies associated with handling large textual datasets derived from reviews, comments, tweets, and posts. This study addresses this challenge by presenting a straightforward architecture, the Double Path Transformer Network (DPTN), designed to model both global and local information for comprehensive review categorization. To enhance the synergy between the attention path and the convolutional path, the study advocates a parallel design that combines a robust self-attention mechanism with a convolutional network. The research employs the gaining-sharing knowledge optimization (GSK) approach to fine-tune hyperparameters, thereby improving the model's classification accuracy. Additionally, the investigation demonstrates that optimization algorithms and deep learning collaboratively manage class imbalances with finesse, even in the absence of explicit measures for such concerns. In the experiment analysis of the proposed model ultimately achieved an accuracy of 95.

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Section: Motivation Of the Research Workmentioning

confidence: 99%

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Kumar,

Thatha,

Udayaraju

et al. 2024

IEEE Access

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show abstract

“…The VADER tool uses both qualitative and quantitative methods to analyze and validate data. [16]- [28]. Subsequently, the data validation is attuned to sentiments using microblog -like information.…”

Section: Related Workmentioning

confidence: 99%

FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis

Rodriguez

Argueta²

2022

IEEE Access

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Hate speech is a form of expression that assaults a person or a community based on race, origin, religion, sexual orientation, or other attributes. Although it can be expressed in multiple ways online and offline, the increasing popularity of social media has exponentially increased both its use and severity. The target of this research, therefore, is to locate and analyze the unstructured data of chosen social media posts that intend to spread hate in the comment sections. To do so, we propose a novel framework based on data science tools, with the objective of sensitizing all social media providers to the pervasiveness of hate on social media. We use sentiment and emotion algorithms to analyze recent posts and comments on those pages. Posts suspected of containing dehumanizing words are preprocessed before being fed to a K-means clustering algorithm, one of the simplest and most popular unsupervised machine learning methods. The proposed framework for automatic detection of hate speech (FADOHS) surpasses the most recent methods identified in A Survey on Automatic Detection of Hate Speech in Text in terms of precision, recall, and F1 scores by 10%.

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Analyzing Customer Sentiments: A Comparative Evaluation of Large Language Models for Enhanced Business Intelligence

Beránek,

Merunka

2024

Lecture Notes in Business Information Processing

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A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

Cited by 8 publications

References 26 publications

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

FADOHS: Framework for Detection and Integration of Unstructured Data of Hate Speech on Facebook Using Sentiment and Emotion Analysis

Analyzing Customer Sentiments: A Comparative Evaluation of Large Language Models for Enhanced Business Intelligence

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