Sentiment Classification based on Machine Learning Approaches in Amazon Product Reviews (original) (raw)
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A Sentiment Analysis of Customer Product Review Based on Machine Learning Techniques in E-Commerce
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The use of customer evaluations as a basis for purchasing decisions has grown in importance with the meteoric rise of e-commerce in the last several years. Reviews provide businesses with valuable information and foster trust, in addition to helping potential customers make informed decisions. A comprehensive examination of a dataset including reviews from Amazon that spans many product categories was part of this research. This SA project's primary goal is to categorise Amazon reviews as neutral, negative, or positive using a BERT model. A study involves several key stages, including data preprocessing, collection, splitting, model training, and evaluation. The JSON-formatted reviews of items sold by Amazon were compiled, including devices like mobile phones, laptops, televisions, tablets, and video security systems. Preprocessing steps included lowercasing, removal of stop words, punctuation, contractions, tokenization, and part-of-speech tagging. Sentiment scores were generated using an opinion lexicon, and word embeddings were applied for numerical vectorization. Employing a cross-entropy loss function trained inside the PyTorch framework, a BERT model was used for sentiment categorisation. To measure how well the model performed, we employed evaluation criteria like recall, F1-score, precision, and accuracy. BERT achieved superior results compared to logistic regression and decision tree models, demonstrating its ability to capture long-term dependencies in textual data. Empowering e-commerce platforms to make educated judgements and expand their service offerings, these results have substantial practical consequences and will give them confidence in their strategy.
A Supervised Learning Technique for Classifying Amazon Product Reviews based on Buyers Sentiments
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A number of applications using internet provide vary essential services. Among them social media and ecommerce platforms are very common. These platforms include a large amount of data which is generated by the users. That data is available in the form of opinion about some post or review. Means the text with emotions which is contain the buyers or user sentiment about some kind of product or service. In this presented work a data mining model is introduced that offers sentiment based text classification for the Amazon product reviews. The proposed data model first preprocesses data and extract the actual review text, in next phase of preprocess the stop words and special characters are removed. The refined text is further utilized with two feature selection techniques first is based on TF-IDF which is used for selecting intense keywords from the review text. Additionally the second feature is selected using NLP text parser. That parser basically performs the POS (Part Of Speech) tagging of review text. Using the obtained POS tags the NLP feature is contracted. Both the features are combined in next and two supervised learners are used namely SVM (support vector machine) and SVR (support vector regression). The experimental results of both the model is measured and compared. The performance study demonstrates the proposed SVM based classifier performs classification accurately and efficiently as compared to SVR based classifier.
An Integrated Approach for Amazon Product Reviews Classification Using Sentiment Analysis
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Sentiment Analysis plays a huge role in business analytics and situations in which text needs to be analyzed. It is used in anticipating market progression based on different news, online blogs and social media opinions. Essential part of information-gathering for market research is to find the opinion of people about the product. Many business enterprises are utilizing these opinions to perform better in the market. In this paper, the analysis is done on the Amazon product‟s reviews dataset. The data is organized through preprocessing and after cleaning through various techniques, some useful features are selected and sentiment analysis is done to generate a sentiment polarity. Various different learning techniques like Naïve Bayes, Linear Support Vector Machine and Logistic Regression classifiers are applied on the preprocessed data and comparison analysis is done to find the best classifier fit for the reviews data through the detailed analysis and generation of the Receiver oper...
Amazon Product Review Sentiment Analysis with Machine Learning
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Users of Amazon's online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy.
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The sentiment is something that expresses the feeling about anything. This work proposes a Machine Learning approach that performs the classification of customer product reviews by finding the sentiment of the review dataset making it easy for product owners to track and improve their quality of the product rather than going through each piece of ratings and review. Amazon Product Reviews dataset is used for training and predicting systems, aiming to achieve maximum accuracy from the algorithm. This work also uses two different modules like Count Vectorizer and Tf-IDF Vectorizer for comparing the accuracy and consistency in training the text dataset, to conclude the better one between them.
Sentiments Analysis of Amazon Reviews Dataset By using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Any opinion of a person that can convey emotions, attitudes, or opinions is known as a sentiment. The data analyzes that are collected from media reports, consumer ratings, social network posts, or microblogging sites are classified as opinion mining research. Analysis of sentiment should be viewed as a way of evaluating people for particular incidents, labels, goods, or businesses. The amount of views exchanged by people in micro-logging sites often increases, which makes nostalgic interpretations more and more common today. All sentiments may be categorized as optimistic, negative, or neutral under three groups. The characteristics are derived from the document term matrix using a bi-gram modeling technique. The sentiments are categorized among positive and negative sentiments. In this analysis, the Python language is used to apply the classification algo for the data obtained. The detailed accomplishment of LinSVC demonstrates greater precision than other algos.
Sentiment Analysis of Amazon Product Reviews using Supervised Machine Learning Techniques
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Today, everything is sold online, and many individuals can post reviews about different products to show feedback. Serves as feedback for businesses regarding buyer reviews, performance, product quality, and seller service. The project focuses on buyer opinions based on Mobile Phone reviews. Sentiment analysis is the function of analyzing all these data, obtaining opinions about these products and services that classify them as positive, negative, or neutral. This insight can help companies improve their products and help potential buyers make the right decisions. Once the preprocessing is classified on a trained dataset, these reviews must be preprocessed to remove unwanted data such as stop words, verbs, pos tagging, punctuation, and attachments. Many techniques are present to perform such tasks, but in this article, we will use a model that will use different inspection machine techniques.
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Nowadays, the world is becoming digitalized e-commerce is ascending in this digitalized world through the availability of products within reach of customers. Furthermore, e-commerce websites allow people to convey their thoughts and feelings. People are increasingly relying on the experiences of other customers. Our opinions and purchasing decision-making are affected by the experience of others and their feedback about products. We always ask others about their opinion to get benefit from their experience; hence, the importance of reviews has grown. However, it is almost impossible for customers to read all such reviews; therefore, sentiment analysis is essential in analyzing them. This study proposes a sentiment analysis to predict the polarity of Amazon baby product dataset reviews using supervised machine learning algorithms. Further, it will allow companies to improve their products by knowing customers' opinions and needs. Amazon is one of the e-commerce giants that people use daily for online purchases where they can read thousands of reviews dropped by other customers about their desired products. These reviews provide valuable opinions about a product such as its property, quality, and recommendations, which helps the purchasers understand almost every detail. This project considers the sentiment classification problem for online reviews using supervised approaches to determine the overall semantics of customer reviews by classifying them into positive and negative sentiments.