Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews

Li, Xuechun; Sun, Xueyao; Xu, Zhiwu; Zhou, Yifan

doi:10.1109/icispc53419.2021.00024

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…The principle behind it resembles the natural language processing sentiment analysis, where researchers would use the sentiment of words to predict the emotion of the sentences. This can be achieved either through a sentiment lexicon to clearly mark negative/positive words, or using machine learning techniques like Word2Vec to extract word features [22]. In the case of melanoma detection, where the same patient provided several skin lesion images, each image's predicted class can be used as a 'skin lexicon' to infer the class of the patient, i.e., whether the patient has cancer.…”

Section: Model Designmentioning

confidence: 99%

A Deep learning based patient care application for skin cancer detection

Dong

Farid

2022

Preprint

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Melanoma is one of the deadliest skin cancer. It can, however, be cured with a high success rate if found in the early stage. With the recent development in Artificial Intelligence (AI), there has been an increase in the research of Machine Learning (ML) and Deep Learning (DL) models for melanoma detection. However, such deployments are primarily trial-based and still waiting to gain wider acceptance from the practitioners. One of the primary reasons for such lower acceptance lies in the inherent approach and ethical concerns. To this end, our study designs a Deep Learning based recommendation application for the patients to advise whether they need to see a dermatologist based on provided skin lesion image. The traditional AI-based melanoma classifiers tend to be developed based on bulk image training and hence would classify images on an individual level. However, they neglect that a single skin lesion is merely a part of the patient. Thereby this research considers the contextual information between different lesions on the same patient. It produces a personalized patient classification rather than focusing on lesion level prediction. Several models are developed using ML and DL techniques, such as transfer learning and deep neural networks. These models are then tested on a real melanoma dataset with the true label provided. External benchmark is also established from recent similar studies using the same dataset. The best classifier achieves an 81.6% Area Under the Receiver Operating Characteristic Curve (AUROC) score using the patient's unique biological features.

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Section: Model Designmentioning

confidence: 99%

A Deep learning based patient care application for skin cancer detection

Dong

Farid

2022

Preprint

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“…In Lexicon-based method, employs Sentiment Dictionary using opinion word and matches it with the information to define the Polarity. The Language Processing Algorithm is utilized for extracting Features such as Phrases, Word Frequency, Parts of Speech Tags, and Opinion Words [7]. But the Supervised Machine Learning (ML) algorithm learns the Polarity (Positive, Negative, or Neutral) of the Reviews from a data that is primarily categorized by a human [8].…”

Section: Introductionmentioning

confidence: 99%

Twitter Text Sentiment Analysis of Amazon Unlocked Mobile Reviews Using Supervised Learning Techniques

Bharathi¹,

R.²,

R.³

2022

INDJCSE

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Due to the drastic increase in usage of Internet and e-commerce, online shopping becomes very common in day to day lives. One of the popular e-commerce websites is Amazon, which allows users to post Reviews and Ratings regarding the purchased products. It finds useful for other customers and companies to improve the product quality. Since the manual extraction of sentiments from User Product Reviews is a tedious process, accurate and Automated Sentiment Analysis (SA) tools are essential. This research work aims to examine the performance of different Machine Learning (ML) models in analyzing the sentiments of Amazon Product Reviews. Initially, the Product Reviews are pre-processed in different ways to transform the data into a useful format. Besides, Term Frequency -Inverse Document Frequency (TF-IDF) vectorizer is used to derive Feature Embeddings. Finally, three Machine Learning (ML) models namely Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Support Vector Machines (SVM) are used for Sentiment Analysis (SA). The Performance Validation of the Machine Learning (ML) models is performed using Benchmark Dataset from Kaggle Repository. The Experimental Results reported that the Support Vector Machine (SVM) model has resulted in better Performance over the other Machine Learning (ML) models interms of Different Measures.

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