Sentiment Analysis Of Amazon Fine Food Reviews

Yarkareddy, Sruthi; Sasikala, T.; Santhanalakshmi, S.

doi:10.1109/icssit53264.2022.9716410

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Amongst the possible usages of NLP the we have Text Classification which aims to classify the topic of a text; Content Filtering that can for example determine if a text contains an abusive message; Sentiment analysis which pertains to judge the polarity of a text, if it conveys a positive ou negative message. Sentiment analysis or opinion mining is the process of identifying the feeling or emotion expressed in the text or document (Yarkareddy et al, 2022); Language modeling aims at predicting the next word in an incomplete sentence; Translation which ams to translate a tex from one language to another; and Summarization that has the objective of reducing the main ideas of a text or document to a version with less words (Chollet, 2021).…”

Section: Resultsmentioning

confidence: 99%

Natural Language Processing and its applications in e-business

Sousa

2022

CIMNE

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The advent of big data and the ability to extract insights from unstructured data has opened new avenues for companies. In this scientific article we have gathered different examples of application of Natural Language Processing (NLP) specifically in the electronic business environment. The main objective was to investigate what kind of analyzes and techniques are being used to extract knowledge from Big data, more concretly unstructured data, and within this type, textual data and what concrete applications derive from NLP in the context of e-business. Being NLP a type of Artificial Intelligence (AI) that uses Machine Learning (ML) and Deep Learning (DL) with the objective of developing a technology that learns, and takes decisions based on what it learned, it was important to understand what a ML workflow is like and therefore, the literatuere revision is very focused on this point. It was concluded that NLP is a complex process whose applications are diverse and relevant in the context of Electronic Business. Today, NLP is used to complete tasks such as text classification, content filtering, sentiment analysis, language modeling, translation, and summarization and applications such as chatbots, voice assistants and recommendation systems. In the future, it is expected that the NLP will have greater influence in different business areas such as the management of new products and recommendation systems; segmentation, segmentation and analysis of customers and users; brand positioning, communication and marketing; competitor analysis; and risk management, sustainability and social responsibility. Specialists in the area also believe that there will possibly be a paradigm shift in AI that will use Reinforcement Learning techniques, which will allow the development of more advanced, adaptive and multipurpose AI agents. It is also to be expected that humans and machines cohabit in a more collaborative way.

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Section: Resultsmentioning

confidence: 99%

Natural Language Processing and its applications in e-business

Sousa

2022

CIMNE

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“…The Data-set is selected as ne food reviews, the data set holds half a million records that are collected on the based on last past ten years of customer reviews published on amazon up till 2012 [10].…”

Section: Fine Food Reviewsmentioning

confidence: 99%

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Alzahrani

2024

Preprint

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Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, amazon at customer churn Support Vector Machine performed better as in overall comparison.

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“…The Data-set is selected as fine food reviews, the data set holds half a million records that are collected on the based on last past ten years of customer reviews published on Amazon up till 2012 [10]. Actually, it holds so many records so while using this data set one would remain unable to see duplication.…”

Section: Fine Food Reviewsmentioning

confidence: 99%

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Alzahrani

2024

JDAIP

View full text Add to dashboard Cite

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

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Sentiment Analysis Of Amazon Fine Food Reviews

Cited by 13 publications

References 20 publications

Natural Language Processing and its applications in e-business

Natural Language Processing and its applications in e-business

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

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