Sentiment analysis of Amazon product reviews based on NLP

Xiao, Yuanhang; Qi, Chengbin; Leng, Hongyong

doi:10.1109/aemcse51986.2021.00249

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…An ever-growing role for machine learning in sentiment analysis has emerged within the last decade [11]. There has been a meteoric rise in the application of sentiment analysis with the introduction and development of deep learning procedures.…”

Section: Advanced Artificial Intelligence Techniques For Sentiment An...mentioning

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: Advanced Artificial Intelligence Techniques For Sentiment An...mentioning

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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“…Moreover, a study of restaurant reviews applied sentiment analysis to evaluate both the service of the restaurant and the experience of the consumer, intending to achieve a win-win situation for the server and consumer and aim for the maximum of economic benefit [5]. Another study used Natural Language Processing to evaluate the comments reviews for the Amazon product, and the researchers found the preference of the product and what characters is most directly related to the rating of the products [6]. Although these studies investigated in different industries, they all share some similarities for the application of NLP: NLP is used to reflect the quality of the service or product and the experience of the consumer.…”

Section: Application Of Natural Language Processingmentioning

confidence: 99%

Effects of Industrial Protection Policies on Firms’ Incentives and Consumer Welfare

Song¹,

Wu²

2022

BCPBM

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Industrial protection policies have been widely implemented to protect domestic firms from foreign competition. This project specifically focused on examining the effects of the Domestic Movie Protection Month Policy that started in China in 2014. First, I demonstrated the existence of this policy by measuring the obvious delay of foreign movies during the summer (July and August) after 2014. Second, I compared the quality of domestic movies released during the summer (treatment group) with movies released at other times of the year (control group) to evaluate the effect of the policy. Two proxies for movie quality and consumer welfare were constructed for robustness checks: (1) Douban rating, which is the general rating of the entire population of the audience on Douban website, the biggest movie rating website in China; (2) Welfare Index which is based on the sentiment analysis constructed by the NLP (Natural Language Processing) of around 70,000 consumers’ comments. The final result showed that the Douban rating and Welfare Index were both significantly higher for domestic movies released during the summer after 2014 than before 2014, while the quality of movies in the control group remained unchanged. This finding supports the hypothesis that domestic firms will have more incentives to invest in quality upgrading if the industrial protection policy is implemented. This conclusion also adds to the recent heated discussions on whether policymakers shall continue to implement industrial protection policy not only in the movie industry but also in other industries that face severe foreign competition.

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“…This section presents the results of the classic schema polarization analysis based on user reviews on the Amazon ecommerce website (Xiao et al, 2021). The criteria for compositional sentiment were set by Zhang et al To find out how much textual sentiment there is.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Analysis on Amazon Product Reviews

Abubakar,

Shahzad,

Abbasi

2021

Int. J. Inform. Inf. Sys. and Comp. Eng.

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The focus of this paper was on Amazon product reviews. The goal of this is to study is two (NLP) for evaluating Amazon product review sentiment analysis. Customers can learn about a product's quality by reading reviews. Several product review characteristics, such as quality, time of evaluation, material in terms of product lifespan and excellent client feedback from the past, will have an impact on product rankings. Manual interventions are required to analyse these reviews, which are not only time consuming but also prone to errors. As a result, automatic models and procedures are required to effectively manage product reviews. (NLP) is the most practical method for training a neural network in this era of artificial intelligence. First, the Naive Bayes classifier was used to analyse the sentiment of consumer in this study. The (SVM) has categorizeduser sentiments into binary categories. The goal of the approach is to forecast some of the most important characteristics of an amazon-based product reviews, and then analyse Customer attitudes about these aspects. The suggested model is validated using a large-scale real-world dataset gathered specifically for this purpose. The dataset is made up of thousands of manually annotated product reviews gathered from amazon. After passing the input via the network model, (TF) and (IDF) pre-processing methods were used to evaluate the feature. The outcomes precision, recall and F1 score are very promising

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Sentiment analysis of Amazon product reviews based on NLP

Cited by 11 publications

References 8 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

Effects of Industrial Protection Policies on Firms’ Incentives and Consumer Welfare

Aspect-Based Sentiment Analysis on Amazon Product Reviews

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