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Alsaedi, Tahani, Muhammad Rizwan Rashid Rana, Asif Nawaz, Ammar Raza, and Abdulrahma Alahmadi. "Sentiment Mining in E-Commerce." International journal of electrical and computer engineering systems 15, no. 8 (2024): 641–50. http://dx.doi.org/10.32985/ijeces.15.8.2.
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Sentiment analysis is crucial for comprehending customer feedback and enhancing workplace culture, as well as improving products and services. By employing natural language processing (NLP) techniques to meticulously analyze this feedback, organizations can identify specific areas that require improvement, address employee issues, and cultivate a positive work environment. These deep learning models powered by NLP offer invaluable tools for HR and sales departments in the e-commerce sector, enabling them to track sentiment trends among employees and users over time and implement targeted interventions. Focusing on the e-commerce industry, this study employs NLP-driven deep learning methodologies to analyze both employee and user feedback, with the objective of identifying underlying sentiments. The proposed framework leverages these advanced techniques to categorize user feedback into positive, negative, or neutral sentiments. This approach aims to develop a robust and effective system for sentiment analysis, providing significant insights that can help drive organizational improvements and enhance customer satisfaction. The key steps of this framework include data collection, NLP-enhanced feature extraction, sentiment detection, and final classification using finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback collected from an e-commerce industry. Evaluation metrics such as accuracy, precision, and recall were utilized to assess the performance of the system. The results demonstrate the effectiveness of the proposed framework, achieving a 93.75% accuracy rate and surpassing existing benchmark methods. The outcomes of this study are particularly consequential for the e-commerce sector, offering them a strategic advantage in refining their product portfolios and cultivating a more dynamic workplace culture
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Gayki, Miss Yashashri, Mr Pratham Chatke, Miss Rani Nandane, et al. "A Survey on “Sentiment Analysis of E-commerce Website’s Reviews”." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1627–31. http://dx.doi.org/10.22214/ijraset.2023.56255.
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Abstract: Sentiment analysis, a vital component of natural language processing, has gained significant relevance in the realm of ecommerce websites. In this digital age, where consumers heavily rely on online reviews to inform their purchase decisions, understanding and harnessing sentiment in ecommerce reviews is paramount. This abstract explores the utilization of sentiment analysis techniques to extract valuable insights from customer feedback, offering a panoramic view of its applications, challenges, and implications. We delve into keyword extraction, sentiment polarity classification, and the integration of sentiment analysis into recommendation systems. This paper also examines the evolving role of sentiment analysis in enhancing user experiences, brand reputation management, and product development. By decoding the sentiments hidden within ecommerce website reviews, businesses can strategically adapt, improve customer satisfaction, and thrive in a highly competitive online marketplace
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Simran, Sethi. "AI-Powered Sentiment Analysis in E-Commerce." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 10, no. 3 (2022): 1–8. https://doi.org/10.5281/zenodo.15029793.
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With the growth of the e-commerce sphere, analysing clients’ customer reviews and social media impressions have been termed as one of the most important tools of business – sentiment analysis – the process of extracting and understanding the inner meaning of emotions expressed in text. This piece discusses different techniques of AI based sentiment analysis in e-commerce lexicon based, Machine learning, deep and transformer neural networks. A review of major applications is provided such as real-time sentiment analysis, categorization, recommendation systems, and enhancement of product design. The authors also summarize practical experiences while developing a tweet-sentiment scoring tool for Snapdeal, analysing what issues still need to be tackled and what directions are possible for further development.
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Tumanggor, Gavrila Louise, and Feliks Victor Parningotan Samosir. "Sentiment Analysis in E-Commerce: Beauty Product Reviews." Ultimatics : Jurnal Teknik Informatika 16, no. 2 (2025): 108–16. https://doi.org/10.31937/ti.v16i2.3708.
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The increasing popularity of online shopping platforms is fueling the need for automated sentiment analysis for product reviews. This research aims to build an automatic sentiment analysis model in Indonesian for e-commerce product reviews. This model is expected to help consumers make purchasing decisions more quickly. We utilize the IndoBERT model, which has shown to be quite effective for general sentiment analysis, achieving an evaluation accuracy of 66.2% despite a high evaluation loss of 0.8006. The approach used combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. It is hoped that this research will be useful for consumers, shop owners, and researchers in efficiently understanding the sentiment of e-commerce product reviews.
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Thakare, Neha V. "Aspect Based Sentiment Analysis for E-Commerce Shopping Website." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1819–22. http://dx.doi.org/10.22214/ijraset.2021.39117.
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Abstract: Sentiment Analysis is that the most ordinarily used approach to research knowledge that is within the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. a good vary of text data is getting generated within the form of suggestions, feedback, tweets, and comments. E-Commerce portals area unit generating tons of data. Every day within the form of customer reviews. Analyzing E-Commerce data can facilitate on-line retailers to grasp customer expectations, offer an improved searching expertise, and to extend sales. Sentiment Analysis can be used to identify positive, negative, and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Keywords: Sentiment analysis, Sentiment classification, Feature selection, Emotion detection, Customer Reviews;
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Jahan, Nusrat, Jubayer Ahamed, and Dip Nandi. "Enhancing E-commerce Sentiment Analysis with Advanced BERT Techniques." International Journal of Information Engineering and Electronic Business 17, no. 3 (2025): 49–61. https://doi.org/10.5815/ijieeb.2025.03.04.
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Liu, Junzhi. "Method to Facilitate E-Commerce Buying Power by Using Machine Learning Techniques." Highlights in Business, Economics and Management 10 (May 9, 2023): 329–36. http://dx.doi.org/10.54097/hbem.v10i.8116.
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The incremental internet usage triggers the rising of e-commerce, a burgeoning shopping mode. Unlike other papers which focus primarily on the technical construction of a sentiment classification model, this paper combines machine learning techniques with business strategies. It aims to determine how sentiment analysis facilitates businesses’ improvement of offerings on e-commerce platforms, increasing customers’ buying power. First, the paper defines consumer sentiment analysis, summarizes the methods different scholars used when classifying sentiment on aspect level, and points out how sentiment analysis is valuable to both businesses and customers. Second, the paper describes an e-commerce notebook, which covers how sentiment analysis can be carried out using data from Olist online retailing store in Brazil. Naïve Bayes and Logistic Regression are utilized when implementing sentiment classification. Finally, according to the word cloud for positive and negative words in reviews, the paper gives some coming-up suggestions for tackling with the most frequently appeared complaint - the delivery time. Businesses can decompose the supply chain into six sub-systems, and adopt computer vision and GIS system in the packaging management system and delivery management system respectively to squeeze the delivery time.
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Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.
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We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social networking sites we developed neural network based algorithms along with tailoring and loss function which carry feelings. This research apply embedding’s to word-level, sentence-level sentimental analysis and classification, constructing sentiment oriented lexicons. Experimental analysis and results addresses that sentiment embedding techniques outperform the context-based embedding’s on many distributed data sets. This work provides familiarity about neural networks techniques for learning word embedding’s in other NLP tasks.
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Abdul Rahman, Zaireen, Bazilah A. Talip, and Husna Sarirah. "Exploring Customer Review of Local Agriculture Product Acceptance in Malaysia: A Concept Paper on Sentiment Mining." International Journal on Perceptive and Cognitive Computing 10, no. 1 (2024): 29–39. http://dx.doi.org/10.31436/ijpcc.v10i1.418.
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Online consumer reviews in e-commerce are one technique to gather consumer opinion and sentiment about a company's products and services. However, manual analysis is impractical due to natural language text's enormous volume and complexity. Text mining and sentiment analysis methods based on machine learning provide an opportunity to analyze data for marketing objectives by increasing sales, positive electronic word-of-mouth (e-WOM), and meeting consumer demands and wants through the enhancement of market offerings. Despite the numerous benefits of analyzing e-commerce reviews to assist a company's marketing strategy, very little research has focused on sentiment and acceptance for Malaysia’s local agriculture products due to mixed language (English-Malay language) processing challenges. This concept paper highlights the use of text mining techniques to extract valuable insights from e-commerce comments related to Malaysian local agriculture products. By leveraging text mining, the study aims to better understand consumer sentiments, preferences, and feedback regarding local products, thereby facilitating improved market analysis and decision-making processes.
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Verma, Nishant, Sumesh Sood, Kritika Kumari, and Neha Kumari. "The Impact of Product Reviews on E-Commerce Performance: A Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1258–64. http://dx.doi.org/10.22214/ijraset.2023.54849.
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Abstract: Sentiment analysis of product reviews has become an important research area in recent years. With the rise of ecommerce platforms, online reviews have become an essential part of the decision-making process for consumers. This paper presents a review of the recent advancements in sentiment analysis techniques for product reviews. The paper covers various aspects of sentiment analysis, such as feature extraction, sentiment classification, and aspect-based sentiment analysis. This paper is to analyse the strengths and weaknesses of different techniques, such as rule-based approaches, machine learningbased approaches, and deep learning-based approaches. The paper also highlights the challenges in sentiment analysis, such as handling negation, sarcasm, and irony in reviews. Furthermore, the paper discusses the future research directions in this field. Finally, this paper conclude with a discussion on the potential applications of sentiment analysis, such as market research, product development, and customer service. Overall, this paper provides an overview of the recent advancements in sentiment analysis techniques for product reviews and serves as a roadmap for future research in this field
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Amrithkala M Shetty and Manjaiah D.H. "Analyzing sentiments in e-commerce: Techniques, applications and challenges." International Journal of Science and Research Archive 12, no. 2 (2024): 2307–70. http://dx.doi.org/10.30574/ijsra.2024.12.2.0843.
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Sentiment Analysis (SA), a crucial component of Natural Language Processing (NLP), involves discerning sentiment within textual data. While widely applicable across diverse domains, this paper specifically focuses on SA within E-Commerce datasets. Serving as a concise review for beginners, the paper encompasses an overview of approximately twenty early works in SA. It elucidates the techniques employed, SA levels, and delineates the methodologies underpinning these approaches. In addition to elucidating the foundational methodologies, this paper provides insights into the applications of SA. It sheds light on how SA techniques find practical utility in discerning and interpreting sentiment nuances within the realm of E-Commerce. Furthermore, the paper delves into the challenges intrinsic to SA, offering a comprehensive understanding of the intricacies associated with this NLP task.
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. Sumalatha, Dr P. "Hybrid Machine Learning Approaches for Enhanced Sentiment Analysis." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03770.
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Abstract—This paper presents a hybrid sentiment analysis system that integrates lexicon-based, traditional machine learning, and deep learning techniques to classify textual data into positive, negative, or neutral sentiments. The system leverages Python-based libraries such as Scikit-learn, NLTK, and Transformers to pre- process text, extract features, and apply models in- cluding Support Vector Machine (SVM), Bidirectional Encoder Representations from Transformers (BERT), Linear Regression, and Valence Aware Dictionary and sEntiment Reasoner (VADER). The framework pro- cesses customer reviews from e-commerce platforms (Amazon, Flipkart) and social media (Instagram) using web scraping and provides actionable insights through sentiment summarization and visualizations (bar and pie charts). Experimental results demonstrate BERT’s superior performance with 92.3% accuracy, followed by SVM (85.6%), Linear Regression (81.2%), and VADER (76.8%). The system addresses challenges like sarcasm, class imbalance, and scalability, offering a scalable, user-friendly solution for real-world applications in e- commerce, social media analytics, and brand reputation management. Index Terms—Sentiment Analysis, Machine Learning, Deep Learning, Natural Language Processing, BERT, SVM, VADER, Text Classification, Data Visualization.
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Ashwath, K., and J. Vinoth. "Aspect Based Sentiment Analysis for E-Commerce Using Classification Techniques." IJISE 1, no. 1 (2019): 8. https://doi.org/10.5281/zenodo.2616501.
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Tremendous accumulations of shopper audits for items are currently accessible on the Web. These audits contain rich stubborn data on different items. They have turned into an important asset to encourage shoppers in understanding the items preceding settling on buying choices, and bolster makers in fathoming purchaser suppositions to successfully enhance the item contributions. In any case, such audits are frequently sloppy, prompting trouble in data route and information securing. It is wasteful for clients to accumulate general suppositions on an item by perusing all the shopper audits and physically investigating assessments on each survey. In this undertaking, we can actualize item surveys rating from item audits, which intend to naturally distinguish critical item perspectives from online buyer surveys. The imperative viewpoints are recognized by two perceptions: the vital parts of an item are typically remarked by an expansive number of shoppers; and buyers' conclusions on the essential angles significantly impact their general sentiments on the item. Specifically, given customer audits of an item, we initially recognize the item angles by marking the surveys and decide buyers' feelings on these perspectives by means of a slant classifier. The Proposed research can be execute SVM and Naive Bayes arrangement to recognize the supposition words by at the same time thinking about the surveys gathering and the impact of purchasers' assessments given to every perspective on their general sentiments. The exploratory outcomes on prevalent portable item surveys show the adequacy of our approach. We additionally apply the survey positioning outcomes to the utilization of assessment order, and enhance the execution essentially.
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Ahmed, Zishan, Shakib Sadat Shanto, and Akinul Islam Jony. "Advancement in Bangla Sentiment Analysis: A Comparative Study of Transformer-Based and Transfer Learning Models for E-commerce Sentiment Classification." Journal of Information Systems Engineering and Business Intelligence 9, no. 2 (2023): 181–94. http://dx.doi.org/10.20473/jisebi.9.2.181-194.
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Background: As a direct result of the Internet's expansion, the quantity of information shared by Internet users across its numerous platforms has increased. Sentiment analysis functions at a higher level when there are more available perspectives and opinions. However, the lack of labeled data significantly complicates sentiment analysis utilizing Bangla natural language processing (NLP). In recent years, nevertheless, due to the development of more effective deep learning models, Bangla sentiment analysis has improved significantly. Objective: This article presents a curated dataset for Bangla e-commerce sentiment analysis obtained solely from the "Daraz" platform. We aim to conduct sentiment analysis in Bangla for binary and understudied multiclass classification tasks. Methods: Transfer learning (LSTM, GRU) and Transformers (Bangla-BERT) approaches are compared for their effectiveness on our dataset. To enhance the overall performance of the models, we fine-tuned them. Results: The accuracy of Bangla-BERT was highest for both binary and multiclass sentiment classification tasks, with 94.5% accuracy for binary classification and 88.78% accuracy for multiclass sentiment classification. Conclusion: Our proposed method performs noticeably better classifying multiclass sentiments in Bangla than previous deep learning techniques. Keywords: Bangla-BERT, Deep Learning, E-commerce, NLP, Sentiment Analysis
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Kamalakannan. R. "Enhanced Customer Emotions Classifications in E-Commerce on Sentiment Analysis with Convolutional Neural Network." Communications on Applied Nonlinear Analysis 32, no. 7s (2025): 908–21. https://doi.org/10.52783/cana.v32.3495.
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In the rapidly evolving e-commerce landscape, accurately interpreting customer emotions from textual reviews is crucial for enhancing user experience and informing business strategies. Traditional sentiment analysis methods often struggle with the complexity and nuance of human emotions expressed in text. This research introduces an advanced approach employing Convolutional Neural Networks (CNNs) to improve the classification of customer emotions in e-commerce platforms. By leveraging CNNs' ability to capture local features and patterns within textual data, our model effectively distinguishes between subtle emotional cues, leading to more precise sentiment categorization. Experimental results demonstrate a significant improvement in classification accuracy over conventional techniques, underscoring the potential of deep learning models in sentiment analysis applications within the e-commerce sector.
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Afan Firdaus, Alfiki Diastama, Rizki Dwi Rahmawan, Yuzzar Rizky Mahendra, and Hasan Dwi Cahyono. "SENTIMENT ANALYSIS CLASSIFICATION IN WOMEN'S E-COMMERCE REVIEWS WITH MACHINE LEARNING APPROACH." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1549–59. https://doi.org/10.52436/1.jutif.2024.5.6.2392.
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User reviews on e-commerce are one of the important elements in e-commerce. User reviews can help potential buyers make decisions based on the experiences and opinions of other people, for example women's e-commerce reviews. In providing positive, neutral or negative sentiment reviews, understanding customer perceptions is challenging. Classifying sentiment reviews will solve this problem, several classification techniques have been carried out, but there is still room for development in the use of simple machine learning techniques and sampling to overcome data class imbalance. Classification techniques used in this paper include Naive Bayes, SVM, and KNN. These algorithms will be compared to determine the most accurate model. Several preprocessing techniques are also carried out such to balance the dataset using ROS and SMOTE. It was obtained that the SVM method with ROS had the highest accuracy of around 0.94 for accuracy value, 0.93 for precision value, 0.94 for recall, and 0.92 for F1-score value. This research shows that the use of sampling techniques such as ROS and SMOTE can be effective in balancing imbalanced datasets, thereby improving model classification performance. These findings can be a reference for developing more efficient and accurate sentiment classification models, especially in the case of imbalanced data.
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Mathur, Ashish. "Sentiment Analysis of Amazon Product Reviews: Leveraging NLP Techniques for Enhanced Classification Accuracy." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 526–34. https://doi.org/10.22214/ijraset.2025.67634.
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This work tackles sentiment classification on product reviews using a hybrid LSTM-GRU model. The primary objective is to evaluate the model's ability to correctly categories sentiments (positive, negative, and neutral) in a massive review dataset including 568,454 rows & 10 columns of product reviews. Data collecting from an online retailer's review system forms part of the approach, then additional pre-processing activities including text cleaning, tokenising, and padding for sentiment analysis preparation follow. Aimed to capture intricate sentiment patterns from textual data, the hybrid LSTM-GRU architecture groups the reviews. The model is defined by its accuracy and loss, and its performance is evaluated using metrics such as recall, F1 score, and precision. A test accuracy of 82.50% is achieved by the model, suggesting high sentiment classification performance, with a loss value of 1.56, according to the results. These results suggest potential for real-time sentiment analysis applications in e-commerce systems since they show that the hybrid LSTM-GRU model efficiently detects sentiment trends inside product reviews. The results highlight the great generalising capacity of the model, thereby reducing prediction error and offering correct sentiment classifications over several review data.
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Abhishek, Kumar Yadav, and Shetteppanavar Puneet. "A Survey of Various Technique used in Aspect Level Sentiment Analysis." Journal of Advance Research in Mobile Computing 3, no. 2 (2021): 1–6. https://doi.org/10.5281/zenodo.5342518.
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In recent years, the vast majority of e-commerce companies have permitted users to write product review. Customers struggle to select the proper product among a plethora of brands. In this situation, sentiment analysis may help extract reviews from e-commerce websites and determine whether product brands are excellent or poor. Sentiment analysis is quickly becoming an indispensable technique for tracking and evaluating people's moods and emotions such as happiness, rage, and sadness. Sometimes merely providing negative and positive evaluations for a product is sufficient. A user may be interested in understanding the polarity of only a subset of a product's features at times. In this instance, aspect-based sentiment analysis allows the user to pick the product attributes of interest in order to acquire summary information about the product feature. The primary goal is to examine various techniques utilized in aspect level sentiment analysis from given text data, such as lexicon, machine learning, and deep learning techniques, in order to uncover aspect based sentiment analysis of provided text data.
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Li, Tingting, Yingli Wu, Yuqing Liu, and Jingqi Li. "Deep Learning-Based Analysis of E-Commerce Enterprises." Journal of Organizational and End User Computing 37, no. 1 (2025): 1–36. https://doi.org/10.4018/joeuc.379722.
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The exponential growth of e-commerce platforms has generated vast amounts of user behavior data, making it increasingly important to predict consumer preferences and spending patterns. Traditional recommendation systems often struggle with challenges such as data sparsity, the cold-start problem, and the inability to capture the dynamic nature of user behavior. These limitations hinder the accurate prediction of consumer actions, especially in evolving markets where user preferences change over time. To address these challenges, the authors propose deep behavioral and sentiment-aware personalized recommendation model, a novel approach that integrates dynamic user behavior modeling and sentiment analysis within a hybrid recommendation framework. The model leverages both collaborative filtering and content-based filtering, enhanced by deep learning techniques, to continuously adapt to evolving user preferences and emotional context, improving both recommendation relevance and consumer spending prediction.
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Nayem Uddin Prince, Mohammed Nazmul Hoque Shawon, Zakaria Kamal Shahed, Riya Islam Nupur, Farzana Akther Mele, and Md Abdullah Al Mamun. "E-commerce clothing review analysis by advanced ML Algorithms." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1680–90. http://dx.doi.org/10.30574/wjarr.2024.24.1.3044.
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A significant aspect in the rapid ascent of Bangladesh's e-commerce sector in recent years has been the significance of consumer evaluations. By examining these reviews, readers can gain enhanced insight into consumer happiness and product quality. This research analyses the sentiment of online clothes reviews with advanced machine-learning techniques. The fundamental purpose of evaluating several machine learning models, such as KNN, RF, XGB, Multi, LSTM, and CNN, is to identify the most effective method for sentiment classification. Following the compilation of an extensive dataset of clothing assessments and the execution of data preparation tasks, including cleaning, tokenization, and stop word elimination, we utilised a combination of deep learning and machine learning models for classification purposes. KNN had the highest accuracy in forecasting the future attitudes of the models evaluated. The findings indicate in the 5000 datasets that KNN is the most effective algorithm for assessing the sentiment of online purchasing reviews. KNN reached at 0.91% Accuracy. This project's automated and scalable methodology may enable online businesses to evaluate client feedback and make more informed decisions. Future research initiatives encompass augmenting the dataset, experimenting with various methods, and incorporating these models into AI-driven mobile and online applications for real-time sentiment analysis.
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Nayem, Uddin Prince, Nazmul Hoque Shawon Mohammed, Kamal Shahed Zakaria, Islam Nupur Riya, and Abdullah Al Mamun Md. "E-commerce clothing review analysis by advanced ML Algorithms." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1680–90. https://doi.org/10.5281/zenodo.15039250.
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A significant aspect in the rapid ascent of Bangladesh's e-commerce sector in recent years has been the significance of consumer evaluations. By examining these reviews, readers can gain enhanced insight into consumer happiness and product quality. This research analyses the sentiment of online clothes reviews with advanced machine-learning techniques. The fundamental purpose of evaluating several machine learning models, such as KNN, RF, XGB, Multi, LSTM, and CNN, is to identify the most effective method for sentiment classification. Following the compilation of an extensive dataset of clothing assessments and the execution of data preparation tasks, including cleaning, tokenization, and stop word elimination, we utilised a combination of deep learning and machine learning models for classification purposes. KNN had the highest accuracy in forecasting the future attitudes of the models evaluated. The findings indicate in the 5000 datasets that KNN is the most effective algorithm for assessing the sentiment of online purchasing reviews. KNN reached at 0.91% Accuracy. This project's automated and scalable methodology may enable online businesses to evaluate client feedback and make more informed decisions. Future research initiatives encompass augmenting the dataset, experimenting with various methods, and incorporating these models into AI-driven mobile and online applications for real-time sentiment analysis.
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Mrs., Ashwini J. Shinde. "Machine Learning Empowered Sentiment Analysis: Techniques and Insights." International Journal of Advance and Applied Research S6, no. 22 (2025): 1145–51. https://doi.org/10.5281/zenodo.15542707.
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<em>Sentiment analysis is a crucial area of contemporary research, particularly useful for examining text data and identifying sentiment elements. E-commerce platforms generate vast amounts of text each day through customer comments, reviews, tweets, and feedback. The rise of social networking sites has significantly enhanced communication and knowledge-sharing. Conducting aspect-based sentiment evaluations can provide businesses with valuable insights into consumer expectations, enabling them to adjust their strategies accordingly. Conveying the precise sentiment of a review can be challenging. This study introduces an approach focusing on the sentimental aspects of product characteristics. We analysed consumer reviews from Amazon and IMDB, using a dataset sourced from the UCI repository, which includes opinion ratings for each review. To derive meaningful information from the datasets and reduce noise, we performed pre-processing steps such as tokenization, punctuation removal, and elimination of whitespace, special characters, and stop words. For effective representation of the pre-processed data, we applied feature selection methods like term frequency-inverse document frequency (TF-IDF). We merged customer reviews from three datasets—Amazon, Yelp, and IMDB—before applying classification using algorithms including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). Finally, we offer insights into potential future work in text classification.</em>
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Gomathy, Dr C. K. "A Comparing Collaborative Filtering and Hybrid Recommender System for E-Commerce." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 635–38. http://dx.doi.org/10.22214/ijraset.2021.38844.
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Abstract: Here we are building an collaborative filtering matrix factorization based hybrid recommender system to recommend movies to users based on the sentiment generated from twitter tweets and other vectors generated by the user in their previous activities. To calculate sentiment data has been collected from twitter using developer APIs and scrapping techniques later these are cleaned, stemming, lemetized and generated sentiment values. These values are merged with the movie data taken and create the main data frame.The traditional approaches like collaborative filtering and content-based filtering have limitations like it requires previous user activities for performing recommendations. To reduce this dependency hybrid is used which combines both collaborative and content based filtering techniques with the sentiment generated above. Keywords: machine learning, natural language processing, movie lens data, root mean square equation, matrix factorization, recommenders system, sentiment analysis
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Kavuru.Pavani, Katam.Kusuma, and Hema Venkata Lakshmi Medicharla. "Analysing Amazon Product Reviews Using Machine Learning." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 2 (2024): 5. https://doi.org/10.5281/zenodo.10753941.
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Reviews from customers are now an integral part of most online e-commerce (Social media , amazon ,Flipkart, meesho) Companies for their daily operations. customers reviews are becoming a major factor in determining what consumer decide to buy. For analyzing the growth of e-commerce companies based on customer reviews or feedback. Organizations typically don’t have the time or resource to scour the internet and read and analyse every piece of data relating to their products, services and brand . Sentiment analysis is an important way for organizations to understand how customers perceive and experience their products and brands. Increasingly, customer feedback is given online through a variety of unconnected platforms, such as Amazon product reviews and posts on social media platforms. In e-commerce Companies, there are huge amount of reviews it is difficult to measure their growth based on these customer reviews .In this we are using SENTIMENT ANALYSIS (SA) is a machine learning algorithm is used to determine whether a given text contains positive(excellent, good ),negative(bad, wrost) or neutral comments(average). Sentiment analysis ,also knowns as opinion mining , is the process of extracting subjective information from text and determining the sentiment expressed within it .It is a text of NPL(natural language processing) . IIn the context of product reviews data , sentiment analysis involves understanding the emotions and opinions of customers towards specific products or brands . In the Sentiment Analysis spilt into various types like Emotion Detection(ED), Aspect Based Sentimental Analysis (ABSA), Fine Grained Sentimental Analysis(FGSA) , Multilingual Sentimental Analysis (MSA).These types are used to analyse the customer reviews is either positive or negative .Emotion detection is the process of identifying human emotion. ED is widely helpful for recognizing the emotions of other .Aspect based sentimental analysis is Breaks down text into aspects (component of products), and then allocates each one a sentimental level(positive ,negative ,neutral).Fine grained sentimental analysis is done at text and sentence level . Multingual sentimental analysis done in multiple languages and also done by the use of complex neural network architecture. The techniques in sentiment analysis are logistic regression, naivebayes, random forest classifier, SVM(support vector machine) etc. By analyzing the sentimental expressed in these reviews, businesses can gain a comprehensive understanding of how their product are perceived by customers. Keywords:- Sentiment Analysis; Logistic Regression ;Naive Bayes ;Random Forest; SVM.
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Hartatik, Hartatik. "Enhancing Customer Experience in E-commerce through Lexicon and TextBlob Sentiment Analysis." West Science Social and Humanities Studies 2, no. 07 (2024): 1237–45. https://doi.org/10.58812/wsshs.v2i07.1747.
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This study evaluates customer satisfaction in business and e-commerce using sentiment analysis based on Indonesian Lexicon and TextBlob. The method used in this study is an explorative quantitative approach with sentiment analysis techniques that compare the Lexicon and TextBlob methods in processing customer review data. The analysis results show the dominance of the neutral sentiment category, with Lexicon producing around 1400 neutral reviews, 1000 positive reviews, and less than 200 negative reviews, while TextBlob shows more than 2000 neutral reviews with less than 500 positive reviews and almost no negative reviews. These findings reveal that the Lexicon method is more sensitive in detecting positive sentiment than TextBlob, which tends to be conservative. The implication of this study is the importance of choosing the right sentiment analysis method to improve customer service strategies. With an accuracy score of 78.52%, precision of 68.11%, and F1-Score of 63.54%, this analysis provides practical insights into how companies can effectively interpret customer sentiment to improve service quality and overall customer satisfaction.
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Sreepal Reddy Bolla. "Sentiment Analysis in Retail Leveraging BERT and NLP Techniques for Customer Insights." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2500–2508. https://doi.org/10.32628/cseit2425482.
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The speedy e-commerce and digital retail expansion generated online reviews and social network posts and feedback surveys among an explosion of customer-generated content. Organizations wanting to gain maximum customer satisfaction along with optimized marketing plans and expanded product lines need to analyze consumer sentiment from unstructured data. Traditional sentiment analysis systems face challenges analyzing contextual meaning as well as detecting sarcasm from unclear language and contextual confusion in both classical machine learning models and rule-based algorithms. Sentiment analysis of retail consumer feedback involves Natural Language Processing (NLP) together with the Bidirectional Encoder Representations from Transformers (BERT). BERT achieves remarkable text categorisation results when it captures sentiments together with deep contextual relationships with greater precision. The research explores ways to boost sentiment prediction capabilities through BERT optimization on datasets that focus on the retail industry. The proposed methodology includes data preprocessing along with feature extraction steps and BERT-based architecture classification methods using DistilBERT, RoBERTa and ALBERT. The performance assessment relies on comparison tests between traditional machine learning models Naïve Bayes and Support Vector Machines (SVM) and LSTMs. The paper addresses essential obstacles which include noisy data management alongside language variant control alongside computational challenges.
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Purohit, Amit. "Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 233–39. http://dx.doi.org/10.22214/ijraset.2021.36202.
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Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.
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Jambhulkar, Prof P. J. "Comparative Analysis of Machine Learning Techniques for Sentiment Classification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem37951.
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Abstract—Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters. Keywords: Decision Tree, Support Vector Machine (SVM), Random Forest, Convolution neural network (CNN), Neural network, Long Short-Term Memory Networks (LSTM), BERT
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Al Ghazali, Nabiel Muhammad, and Yuliant Sibaroni. "Sentiment Classification in E-Commerce Using Naïve Bayes and Combined Lexicon - N-Gram Features." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 10, no. 2 (2025): 1257–71. https://doi.org/10.29100/jipi.v10i2.6157.
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This study investigates sentiment classification in e-commerce using Naïve Bayes with lexicon-based, N-gram, and combined lexicon-N-gram features. While previous research has employed various e-commerce platforms and achieved varying degrees of accuracy using Naïve Bayes for sentiment analysis, the combination of lexicon and N-gram features with Naïve Bayes has not been extensively explored in e-commerce contexts. This study proposes to evaluate three models: Naïve Bayes with Lexicon Features, Naïve Bayes with N-Gram Features, and Naïve Bayes with Combined Lexicon-N-Gram Features. The research analyzes 10,000 customer reviews of the Shopee application from the Google Play Store. Results show that the Naïve Bayes model using combined lexicon-N-gram features achieved the highest performance among the three approaches. Using 10-fold cross-validation, the combined model achieved an average accuracy of 83.4%. The N-gram model showed strong performance with an average accuracy of 82.8%, while the lexicon-based model demonstrated lower performance with an average accuracy of 77%. These findings contribute to the field of sentiment analysis in e-commerce, highlighting the effectiveness of combining lexicon and N-gram features when used with Naïve Bayes classifiers. The study provides insights into optimizing sentiment classification techniques for e-commerce platforms, emphasizing the importance of leveraging both semantic and contextual information in sentiment analysis tasks.
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Enas M. Turki. "Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 269–79. https://doi.org/10.52783/jisem.v10i8s.1025.
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In the dynamic landscape of e-commerce, personalized product recommendation systems are pivotal in enhancing user experiences and driving business success. This study leverages the Amazon Product Reviews dataset, a rich source of user-generated feedback, to design a scalable and effective personalized recommendation system. Adopting a structured methodology encompassing four data analysis phases includes descriptive, diagnostic, predictive, and prescriptive. This research extracts meaningful insights from product reviews and ratings. The study captures user preferences and sentiments using advanced natural language processing (NLP) and machine learning techniques, including sentiment analysis and hybrid recommendation models. Implementing distributed computing frameworks like Apache Spark ensures scalability and operational efficiency. Centered on the electronics category, this research integrates sentiment insights with collaborative and content-based filtering techniques to address challenges like data sparsity and the cold-start problem. The findings contribute to advancing personalized recommendation systems by delivering actionable insights that enhance customer satisfaction, streamline product discovery, and provide significant value to academic research and industry practices.
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Nikita, Ananda Putri Masaling, and Suhartono Derwin. "Utilizing RoBERTa and XLM-RoBERTa pre-trained model for structured sentiment analysis." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 410–21. https://doi.org/10.11591/ijict.v13i3.pp410-421.
Full textAbstract:
The surge in internet usage has amplified the trend of expressing sentiments across various platforms, particularly in e-commerce. Traditional sentiment analysis methods, such as aspect-based sentiment analysis (ABSA) and targeted sentiment analysis, fall short in identifying the relationships between opinion tuples. Moreover, conventional machine learning approaches often yield inadequate results. To address these limitations, this study introduces an approach that leverages the attention values of pretrained RoBERTa and XLM-RoBERTa models for structured sentiment analysis. This method aims to predict all opinion tuples and their relationships collectively, providing a more comprehensive sentiment analysis. The proposed model demonstrates significant improvements over existing techniques, with the XLM-RoBERTa model achieving a notable sentiment graph F1 (SF1) score of 64.6% on the OpeNEREN dataset. Additionally, the RoBERTa model showed satisfactory performance on the multi-perspective question answer (MPQA) and DSUnis datasets, with SF1 scores of 25.3% and 29.9%, respectively, surpassing baseline models. These results underscore the potential of this proposed approach in enhancing sentiment analysis across diverse datasets, making it highly applicable for both academic research and practical applications in various industries.
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Huo, Zheng. "Data statistical analysis on Amazon e-commerce platform for recommender system." Applied and Computational Engineering 51, no. 1 (2024): 97–103. http://dx.doi.org/10.54254/2755-2721/51/20241183.
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Recommendation systems are a crucial element in engaging users and maintaining their engagement with e-commerce platforms. By recommending products or services that are likely to be relevant to each user's interests and preferences, the system can help maintain user interest and encourage them to spend more time on the platform. This work analyzes the principles of several off-the-shelf recommendation models, including the collaborative filtering model, the singular value decomposition model, and the rating-based collaborative filtering model. These models play a crucial role in the field of recommendation systems for e-commerce. To gain further insights from the comments of various merchandise items, sentiment analysis techniques and word cloud analysis are applied. Evaluation of these results demonstrates the critical role of recommender systems in shaping the future landscape of e-commerce. Sentiment analysis allows us to identify patterns in user feedback and understand how different factors influence user satisfaction with products or services. Word cloud analysis provides a visual representation of the most frequently mentioned features or keywords in the comments, allowing us to identify trends and patterns in user behavior. By combining these techniques with traditional recommendation models, more accurate and personalized recommendations could be made that better meet user needs and enhance their shopping experience on e-commerce platforms.
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Lestari, Anjar Ayuning, Ahmad Faqih, and Gifthera Dwilestari. "Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 758–63. https://doi.org/10.59934/jaiea.v4i2.743.
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Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
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UNGAR, Kevin. "DECODING E-COMMERCE FLUCTUATIONS: A MACHINE LEARNING ANALYSIS OF INFLUENTIAL VARIABLES DURING US COVID-19 (2010-2024)." Revista Economica 76, no. 4 (2024): 95–109. https://doi.org/10.56043/reveco-2024-0036.
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This research examines factors influencing the US e-commerce market during the Covid-19 crisis, investigating consumer behavior across three periods: pre-pandemic (2010–December 2019), pandemic (December 2019–2021), and post-pandemic crisis (2022–2024). Using multiple linear regression analysis in Python and Machine Learning techniques, the study evaluates the impact of key economic indicators (Gross Domestic Product, Unemployment Rate, Consumer Price Index, Internet Penetration Rate, and Consumer Sentiment Index) on e-commerce sales. These variables were used to develop a mathematical model explaining the relationship between economic and sentiment indicators and e-commerce growth. During the pandemic, e-commerce activity surged as lockdowns forced consumers to rely more on online shopping. Post-pandemic, as restrictions eased and confidence recovered, the market exhibited continued growth, surpassing pre-pandemic levels. Despite the initial surge driven by restrictions, e-commerce remained strong even after their removal. The analysis highlights the importance of economic factors in shaping e-commerce trends, with GDP and CPI emerging as particularly influential. Additionally, the study underscores the critical role of internet penetration in sustaining e-commerce, especially during physical distancing measures. These findings provide insights into how economic and technological factors drive long-term changes in consumer behavior within the e-commerce sector.
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Ungar, Kevin. "DECODING E-COMMERCE FLUCTUATIONS: A MACHINE LEARNING ANALYSIS OF INFLUENTIAL VARIABLES DURING US COVID-19 (2010-2024)." Revista Economica 76, no. 4 (2024): 95–109. https://doi.org/10.56043/reveco-2024-0036.
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This research examines factors influencing the US e-commerce market during the Covid-19 crisis, investigating consumer behavior across three periods: pre-pandemic (2010–December 2019), pandemic (December 2019–2021), and post-pandemic crisis (2022–2024). Using multiple linear regression analysis in Python and Machine Learning techniques, the study evaluates the impact of key economic indicators (Gross Domestic Product, Unemployment Rate, Consumer Price Index, Internet Penetration Rate, and Consumer Sentiment Index) on e-commerce sales. These variables were used to develop a mathematical model explaining the relationship between economic and sentiment indicators and e-commerce growth. During the pandemic, e-commerce activity surged as lockdowns forced consumers to rely more on online shopping. Post-pandemic, as restrictions eased and confidence recovered, the market exhibited continued growth, surpassing pre-pandemic levels. Despite the initial surge driven by restrictions, e-commerce remained strong even after their removal. The analysis highlights the importance of economic factors in shaping e-commerce trends, with GDP and CPI emerging as particularly influential. Additionally, the study underscores the critical role of internet penetration in sustaining e-commerce, especially during physical distancing measures. These findings provide insights into how economic and technological factors drive long-term changes in consumer behavior within the e-commerce sector.
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S, Badri Narayanan, Chandra Moulee K. V, Desvar K. J, and Vimal V. R. "Opinion Mining For E-Commerce in Social Media Accounts." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 819–26. http://dx.doi.org/10.22214/ijraset.2023.49535.
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Abstract: The usage of the Internet has been growing rapidly, and in the field of natural language processing (NLP), sentiment analysis has become one of the most popular techniques. Using sentiment analysis, the emotional tone behind a body of text can be mined effectively for different occasions. Instagram has become a popular platform for people to buy and sell online. Nonetheless, some studies have found that frauds occurred as a result of buying and selling on the platform, such as when product quantity and specifications differed from what was claimed, a defective product was received, and other frauds involved Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram, and there is a need for a trust model to evaluate the trustworthiness of sellers. Nowadays, people provide their feedback after a long time of usage only on social media platforms like Instagram. Therefore, these comments play a major role in helping people decide if the product will be beneficial for them over a long period of time. Mining such content to evaluate people’s sentiments can play a critical role in making decisions to keep the situation under control. {objective} Six different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 76.7% with logistic regression. This study can be useful to determine the trustworthiness of the company.
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Kodityala, Prasanna Laxmi, Swathi Pamula, Ms.M.Mamatha, Sunder Mr.P.Shyam, Dr.K.Rajitha, and R. Mohan Krishna Mr. "AI-DRIVEN E-COMMERCE REVIEW ANALYSIS USING BERT ALGORITHM." Journal of Advancement in Software Engineering and Testing 8, no. 2 (2025): 31–39. https://doi.org/10.5281/zenodo.15573002.
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<em> This project is designed to provide users with meaningful insights from customer feedback by analyzing reviews collected from popular e-commerce platforms like Amazon and Flipkart. It allows users to input product URLs, from which reviews are automatically extracted using robust web scraping techniques. These techniques are implemented using tools like Selenium and BeautifulSoup to handle dynamic content and retrieve multiple pages of reviews effectively.</em> <em>Once the reviews are collected, the backend processes those using advanced Natural Language Processing (NLP) techniques. The primary goals of this analysis are to perform sentiment classification, extract important keyphrases, and generate concise summaries of user opinions. These NLP tasks are powered by powerful Python libraries such as Hugging Face Transformers, Spacy, and Torch. BERT-based models help in understanding the contextual meaning of the text, while summarization is handled using pretrained models like BART. This enables the system to identify both positive and negative aspects of a product, such as “The phone has a great camera but poor battery life.”</em> <em>The application is built with a modern tech stack. The frontend is developed using React.js, ensuring a responsive and user- friendly interface. The backend ais powered by Node.js, which handles URL input, scraping logic, and connections with the NLP modules. All processed data, including raw reviews, sentiment labels, keyphrases, and summaries, are stored in a MongoDB database for easy access and retrieval.</em> <em>By combining web scraping and AI-powered text analysis, the project offers a smart solution for buyers to evaluate products based on real customer experiences. Instead of going through hundreds of reviews manually, users can now get a concise, clear summary that highlights important features and issues. This project demonstrates the effective use of machine learning and web technologies in solving real-world problems.</em> <em> </em>
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Kim, Tae Yeun, and Hyoung Ju Kim. "Opinion Mining-Based Term Extraction Sentiment Classification Modeling." Mobile Information Systems 2022 (April 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/5593147.
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The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.
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Mery Oktaviyanti Puspitaningtyas, Kartika Puspita, Yuris Alkhalifi, and Yulita Ayu Wardani. "IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA." Jurnal Riset Informatika 7, no. 2 (2025): 30–37. https://doi.org/10.34288/jri.v7i2.362.
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Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
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Chaudhari, Ram. "Customer Sentiment Analysis for Demand Forecasting of Electronic Devices Using Machine Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50049.
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Abstract—The growing influence of e-commerce and social me- dia has made traditional demand forecasting methods insufficient due to their limited responsiveness to real-time consumer senti- ment. This paper presents a sentiment-driven demand forecasting framework that combines web scraping, natural language pro- cessing, and machine learning. Product reviews from Amazon and tweets are extracted using BeautifulSoup, analyzed using BERT, NLTK, SpaCy, and a custom LSTM model. Sentiment scores are integrated into a pooled machine learning model alongside historical sales and seasonal data. Bayesian inference is then applied to perform sentiment-weighted product allocation across regions. Experimental results demonstrate improved forecasting accuracy, supporting more adaptive inventory and marketing strategies. Keywords - Demand forecasting, sentiment analysis, BERT, Bayesian inference, machine learning, NLP.
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Gupta, Ketan, Nasmin Jiwani, and Neda Afreen. "A Combined Approach of Sentimental Analysis Using Machine Learning Techniques." Revue d'Intelligence Artificielle 37, no. 1 (2023): 1–6. http://dx.doi.org/10.18280/ria.370101.
Full textAbstract:
Sentiment analysis is a vital area of current research. The area of sentiment analysis is extensively used for observing text data and identifying the sentiment element. Every day, e- commerce sites produce a massive amount of text information from customer's comments, reviews, tweets, and feedbacks. One of the most recent technological advances in web development is the emergence of social networking websites. It aids in communication and knowledge gathering. Aspect - based evaluation of this information can help businesses to gain a greater understanding of their consumers' expectations and then shape their plans accordingly. It is difficult to convey the exact sentiment of a review. In this study, we demonstrated an approach that focuses on sentimental aspects of the item's characteristics. Consumer reviews on Amazon and IMDB have been presented and evaluated. We obtained the dataset from the UCI repository, where each analysis's opinion rates are first observed. To get meaningful information from datasets, and to eliminate noise, the pre-processing operations are performed by the system such as tokenization, punctuation, whitespace, special character, and stop-word removal. For the purpose of accurately representing the preprocessed data, feature selection methods such as word frequency-inverse document frequency are utilized (TF–IDF). The customer reviews from three datasets Amazon, Yelp, and IMDB is merged and classification is performed using classifiers such as Naïve Bayes, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). In last, we provide some insight into the future text classification work.
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Putri Masaling, Nikita Ananda, and Derwin Suhartono. "Utilizing RoBERTa and XLM-RoBERTa pre-trained model for structured sentiment analysis." International Journal of Informatics and Communication Technology (IJ-ICT) 13, no. 3 (2024): 410. http://dx.doi.org/10.11591/ijict.v13i3.pp410-421.
Full textAbstract:
The surge in internet usage has amplified the trend of expressing sentiments across various platforms, particularly in e-commerce. Traditional sentiment analysis methods, such as aspect-based sentiment analysis (ABSA) and targeted sentiment analysis, fall short in identifying the relationships between opinion tuples. Moreover, conventional machine learning approaches often yield inadequate results. To address these limitations, this study introduces an approach that leverages the attention values of pre-trained RoBERTa and XLM-RoBERTa models for structured sentiment analysis. This method aims to predict all opinion tuples and their relationships collectively, providing a more comprehensive sentiment analysis. The proposed model demonstrates significant improvements over existing techniques, with the XLM-RoBERTa model achieving a notable sentiment graph F1 (SF1) score of 64.6% on the OpeNER<sub>EN</sub> dataset. Additionally, the RoBERTa model showed satisfactory performance on the multi-perspective question answer (MPQA) and DS<sub>Unis</sub> datasets, with SF1 scores of 25.3% and 29.9%, respectively, surpassing baseline models. These results underscore the potential of this proposed approach in enhancing sentiment analysis across diverse datasets, making it highly applicable for both academic research and practical applications in various industries.
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Yuda, Afi Ghufran, Rice Novita, Mustakim, and M. Afdal. "Comparison of Service and Ease of e-Commerce User Applications Using BERT." Jurnal Sistem Cerdas 7, no. 2 (2024): 98–107. http://dx.doi.org/10.37396/jsc.v7i2.403.
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The development of e-commerce has transformed shopping patterns by harnessing the internet, enabling consumers to shop online. In Indonesia, e-commerce has experienced rapid growth, with numerous options such as Tokopedia, Shopee, and Lazada, leading to intense competition. Sentiment analysis using machine learning techniques has become crucial for understanding consumer views on these e-commerce services. This study analyzes user comments on Tokopedia, Shopee, and Lazada e-commerce platforms from Instagram social media, totaling 3900 data points, using the Bidirectional Encoder Representations from Transformers (BERT) model with 5 epochs and a batch size of 32. Sentiment analysis utilizes 3 types of labels: positive, neutral, and negative. The final results of the study include the performance analysis of the BERT model, as well as comparisons for each predefined category, namely Promotions & Offers, and Services. The final results of the model indicate good performance, with accuracy rates of 95%, 97%, and 99%, respectively.
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Kumar, Mahander, Lal Khan, and Hsien-Tsung Chang. "Evolving techniques in sentiment analysis: a comprehensive review." PeerJ Computer Science 11 (January 28, 2025): e2592. https://doi.org/10.7717/peerj-cs.2592.
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With the rapid expansion of social media and e-commerce platforms, an unprecedented volume of user-generated content has emerged, offering organizations, governments, and researchers invaluable insights into public sentiment. Yet, the vast and unstructured nature of this data challenges traditional analysis methods. Sentiment analysis, a specialized field within natural language processing, has evolved to meet these challenges by automating the detection and categorization of opinions and emotions in text. This review comprehensively examines the evolving techniques in sentiment analysis, detailing foundational processes such as data gathering and feature extraction. It explores a spectrum of methodologies, from classical word embedding techniques and machine learning algorithms to recent contextual embedding and advanced transformer models like Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and T5. With a critical comparison of these methods, this article highlights their appropriate uses and limitations. Additionally, the review provides a thorough overview of current trends, insights into future directions, and a critical exploration of unresolved challenges. By synthesizing these developments, this review equips researchers with a solid foundation for assessing the current state of sentiment analysis and guiding future advancements in this dynamic field.
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Dewi, Tursina, Asrianda Asrianda, and Yesy Afrillia. "Sentiment Analysis of Customer Satisfaction Towards Shopee and Lazada E-commerce Platform Using the Random Forest Algorithm Classifier." International Journal of Engineering, Science and Information Technology 5, no. 1 (2024): 229–35. https://doi.org/10.52088/ijesty.v5i1.692.
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In the digital era, e-commerce platforms like Shopee and Lazada have become the primary channels for online transactions in Indonesia, significantly shaping consumer behaviour and business strategies. This study analyses and compares consumer sentiment towards product reviews on these platforms, focusing on three prominent stores: Skintific, Originote, and Azarine. The research utilized a dataset of 4,500 comments collected from both platforms, with 3,600 comments allocated for training and 900 comments for testing. The sentiment analysis used a lexicon-based approach and machine learning techniques to ensure accuracy and reliability. The results reveal that the Skintific store achieved 88% positive sentiment on Shopee and 84.1% on Lazada. The Originote store recorded 81.4% positive sentiment on Shopee and 91.5% on Lazada, while the Azarine store achieved 87.8% on Shopee and 77.9% on Lazada. These findings highlight variations in consumer sentiment between platforms, which platform-specific features and user demographics may influence. This study provides valuable insights for businesses to tailor their marketing strategies and improve customer engagement on different e-commerce platforms.
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Li, Shugang, Fang Liu, Yuqi Zhang, Boyi Zhu, He Zhu, and Zhaoxu Yu. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review." Mathematics 10, no. 19 (2022): 3554. http://dx.doi.org/10.3390/math10193554.
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In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.
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Suresh, Arjun, Bramadathan A S, Hisham A Hashim, and Sruthy K Joseph. "Comprehensive Sentiment and Fake Comment Analysis." Journal of Artificial Neural Networks and Learning System 1, no. 2 (2024): 41–56. http://dx.doi.org/10.46610/joannls.2024.v02i01.005.
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This project introduces an innovative system for analyzing sentiment and detecting fake comments within e-commerce product reviews, aimed at enhancing the credibility and reliability of online feedback essential for consumer purchasing decisions. Our system integrates VADER, a robust rule-based model for general sentiment analysis, with RoBERTa, an advanced transformer-based deep learning model known for its superior accuracy. This dual approach harnesses the complementary strengths of traditional and modern techniques, resulting in a comprehensive and effective solution for sentiment analysis. A critical component of our system is the fake comment detection module, which utilizes sentiment scores and additional indicators to identify fraudulent reviews accurately. This module is essential for ensuring the authenticity of customer feedback and maintaining the integrity of online review platforms. Our analysis uncovers significant trends in sentiment across various product categories, providing valuable insights that can inform targeted marketing strategies and product enhancements. By examining the relationships between sentiment polarity, review ratings, and the likelihood of fake comments, we offer actionable intelligence for e-commerce platforms to improve product quality, enhance customer satisfaction, and safeguard their review systems. This project underscores the importance of advanced tools in upholding the trustworthiness of online reviews. By enabling businesses to understand genuine customer sentiment better and fostering transparency in the digital marketplace, our system enhances the overall consumer experience. Future efforts will focus on refining detection algorithms, exploring additional features for sentiment analysis, and validating the system across diverse datasets and languages to further elevate standards in online review systems.
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Xiong, Bowen, Jianwei Sun, and Xiangwen Pang. "E-commerce Platform in Digital Economy: Analysis and Optimization of Amazon's Footwear Store." Financial Economics Research 1, no. 2 (2024): 90–114. http://dx.doi.org/10.70267/t59bcb11.
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With the continuous deepening of globalization, cross-border e-commerce has become an important part of global trade. Especially driven by digital payment technology, network technology, industrial upgrading and policy support, China's cross-border e-commerce industry has developed rapidly. This research aims to analyze the sales data of men's footwear on the Amazon platform, understand market trends, user behavior patterns, and product characteristics, and then provide data-supported marketing strategies and operational decisions for e-commerce platforms. We have adopted a variety of data analysis techniques, including data cleaning, ARIMA model prediction, price band analysis, repeat purchase rate analysis, user profiling (RFM model), sentiment analysis and cluster analysis. Through comprehensive analysis of multi-dimensional information such as store sales data, product attributes, market hot words, and user evaluations, market dynamics and user preferences are revealed. We have comprehensively applied a variety of data analysis models and techniques, such as using the ARIMA model for sales prediction, the LDA model for sentiment analysis, and the RFM model combined with cluster analysis to construct a refined user profile. Through in-depth analysis of user transaction behaviors and product attributes, accurate marketing strategies can be provided for e-commerce platforms, enhance user experience, increase user stickiness, and thus achieve business growth and improvement of profitability. At the same time, the research also points out the importance of brand building, supply chain management, risk assessment and other aspects, providing strategic suggestions for the long-term stable development of cross-border e-commerce.
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Anvar, Shathik J., and Prasad K. Krishna. "A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques." International Journal of Applied Engineering and Management Letters (IJAEML) 4, no. 2 (2020): 41–67. https://doi.org/10.5281/zenodo.3977576.
Full textAbstract:
Many businesses are using social media networks to deliver different services and connect with clients and collect information about the thoughts and views of individuals. Sentiment analysis is a technique of machine learning that senses polarities such as positive or negative thoughts within the text, full documents, paragraphs, lines, or subsections. Machine Learning (ML) is a multidisciplinary field, a mixture of statistics and computer science algorithms that are commonly used in predictive and classification analyses. This paper presents the common techniques of analyzing sentiment from a machine learning perspective. In light of this, this literature review explores and discusses the idea of Sentiment analysis by undertaking a systematic review and assessment of corporate and community white papers, scientific research articles, journals, and reports. The goal and primary objectives of this article are to analytically categorize and analyze the prevalent research techniques and implementations of Machine Learning techniques to Sentiment Analysis on various applications. The limitation of this analysis is that by excluding the hardware and the theoretical exposure pertinent to the subject, the main emphasis is on the application side alone. The limitation of this study is that the major focus is on the application side thereby excluding the hardware and theoretical aspects related to the subject. Finally, this paper includes a research proposal for e-commerce environment towards sentiment analysis applying machine learning algorithms.
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Elmurngi, Elshrif Ibrahim, and Abdelouahed Gherbi. "Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques." International Journal of Organizational and Collective Intelligence 10, no. 1 (2020): 32–62. http://dx.doi.org/10.4018/ijoci.2020010103.
Full textAbstract:
Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.