Relevant bibliographies by topics / "Sentiment Analysis in E-Commerce: Techniques

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Academic literature on the topic '"Sentiment Analysis in E-Commerce: Techniques'

Author: Grafiati
Published: 7 June 2025
Last updated: 11 July 2025

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Journal articles on the topic ""Sentiment Analysis in E-Commerce: Techniques"

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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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Dissertations / Theses on the topic ""Sentiment Analysis in E-Commerce: Techniques"

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GUERREIRO, ALEXANDRA DOS SANTOS. "ANALYSIS OF THE EFFICIENCY IN THE E-COMMERCE COMPANIES USING TECHNIQUES OF DATA ENVELOPMENT ANALYSIS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9973@1.

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O trabalho tem como foco a análise da eficiência logística em empresas de comércio eletrônico, estudando, em particular, o caso da SomLivre.Com. A eficiência das empresas de comércio eletrônico será comparativamente avaliada utilizando-se a metodologia de Análise Envoltória de Dados (DEA), permitindo assim situar a empresa estudada em relação ao contexto e desempenho do comércio eletrônico nacional. Os resultados da análise DEA são interpretados para a SomLivre.Com, destacando-se fatores ligados aos processos logísticos, principalmente quanto ao atendimento do pedido ou fulfillment, que podem contribuir para ampliar o seu nível de eficiência.<br>The main focus of this M. Sc. thesis is the analysis of the logistics efficiency of e-commerce firms with special emphasis on the case of SomLivre.com. The efficiency of e-commerce companies will be comparatively evaluated using the Data Evaluation Envelopment Analysis (DEA), which allows the evaluation of the company considered in the case study in relation to other competing firms in the same sector. Results obtained and the DEA evaluation are interpreted to the SomLivre.com case with special emphasis on the logistics process factors, specially the fulfillment or order satisfaction, which may contribute to the improvement of its level of efficiency.
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Nafea, Ibtehal T. "Performance modelling and analysis of e-commerce systems using class based priority scheduling : an investigation into the development of new class based priority scheduling mechanisms for e-commerce system combining different techniques." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5730.

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Recently, technological developments have affected most lifestyles, especially with the growth in Internet usage. Internet applications highlight the E-commerce capabilities and applications which are now available everywhere; they receive a great number of users on a 24-7 basis because online services are easy to use, faster and cheaper to acquire. Thus E-commerce web sites have become crucial for companies to increase their revenues. This importance has identified certain effective requirements needed from the performance of these applications. In particular, if the web server is overloaded, poor performance can result, due to either a huge rate of requests being generated which are beyond the server's capacity, or due to saturation of the communication links capacity which connects the web server to the network. Recent researches consider the overload issue and explore different mechanisms for managing the performance of E-commerce applications under overload condition. This thesis proposes a formal approach in order to investigate the effects of the extreme load and the number of dropped requests on the performance of E- III commerce web servers. The proposed approach is based on the class-based priority scheme that classifies E-commerce requests into different classes. Because no single technique can solve all aspects of overload problems, this research combines several techniques including: admission control mechanism, session-based admission control, service differentiation, request scheduling and queuing model-based approach. Request classification is based on the premise that some requests (e.g. buy) are generally considered more important than others (e.g. browse or search). Moreover, this research considers the extended models from Priority Scheduling Mechanism (PSM). These models add a new parameter, such as a review model or modify the basic PSM to low priority fair model, after the discovery of ineffectiveness with low priority customers or to add new features such as portal models. The proposed model is formally specified using the π-calculus in early stage of models design and a multi-actor simulation was developed to reflect the target models as accurately as possible and is implemented as a Java-based prototype system. A formal specification that captures the essential PSM features while keeping the performance model sufficiently simple is presented. Furthermore, the simplicity of the UML bridges the gap between π-calculus and Java programming language. IV There are many metrics for measuring the performance of E-commerce web servers. This research focuses on the performance of E-commerce web servers that refer to the throughput, utilisation, average response time, dropped requests and arrival rate. A number of experiments are conducted in order to test the performance management of the proposed approaches.
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Lawani, Abdelaziz. "THREE ESSAYS ON THE APPLICATION OF MACHINE LEARNING METHODS IN ECONOMICS." UKnowledge, 2018. https://uknowledge.uky.edu/agecon_etds/68.

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Over the last decades, economics as a field has experienced a profound transformation from theoretical work toward an emphasis on empirical research (Hamermesh, 2013). One common constraint of empirical studies is the access to data, the quality of the data and the time span it covers. In general, applied studies rely on surveys, administrative or private sector data. These data are limited and rarely have universal or near universal population coverage. The growth of the internet has made available a vast amount of digital information. These big digital data are generated through social networks, sensors, and online platforms. These data account for an increasing part of the economic activity yet for economists, the availability of these big data also raises many new challenges related to the techniques needed to collect, manage, and derive knowledge from them. The data are in general unstructured, complex, voluminous and the traditional software used for economic research are not always effective in dealing with these types of data. Machine learning is a branch of computer science that uses statistics to deal with big data. The objective of this dissertation is to reconcile machine learning and economics. It uses threes case studies to demonstrate how data freely available online can be harvested and used in economics. The dissertation uses web scraping to collect large volume of unstructured data online. It uses machine learning methods to derive information from the unstructured data and show how this information can be used to answer economic questions or address econometric issues. The first essay shows how machine learning can be used to derive sentiments from reviews and using the sentiments as a measure for quality it examines an old economic theory: Price competition in oligopolistic markets. The essay confirms the economic theory that agents compete for price. It also confirms that the quality measure derived from sentiment analysis of the reviews is a valid proxy for quality and influences price. The second essay uses a random forest algorithm to show that reviews can be harnessed to predict consumers’ preferences. The third essay shows how properties description can be used to address an old but still actual problem in hedonic pricing models: the Omitted Variable Bias. Using the Least Absolute Shrinkage and Selection Operator (LASSO) it shows that pricing errors in hedonic models can be reduced by including the description of the properties in the models.
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Hou, Yi-Heng, and 侯以恆. "A study of the recommendation systems with sentiment analysis of E-commerce based on speech recognition technique." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/nqj395.

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碩士<br>淡江大學<br>資訊管理學系碩士班<br>104<br>According to market investigation report by AC Nielsen in 2013, about 77% of consumers are swayed by opinions and comments posted on the internet by other users.On the perspective of speech recognition development, more and more examples are applied to home appliances, smart devices and public utilities. For example, a system developed by Apple, called “Siri”. It can interact with users through speech recognition inputs, manage user schedules, conference arrangement, phone calling and message sending, or even chat with the user in ways. Now is still widely used. By speech recognition input, it can increase the efficiency and speed of information searching. Speech recognition techniques are mostly applied to language learning like English and Japanese pronunciation practice. Some are applied to internet of things. However, we rarely see the examples of combination with E-commerce and sentimental analysis. Thus, this study attempts to combine the speech recognition and sentimental analysis technique to build a prototype of E-commerce recommendation system. This prototype system automatically collects articles and information of products on the web, catches keywords and sentimental analyzing, and identifies the input of user with speech. Then recommends to user for the best product as a result. Users were asked to take a survey after experiencing a prototype system built for this study. We took feasible feedback as future reference for developing the system. The purpose of this study was to find the most expected speech recognition e-commerce mode of users. The research found that 68% of users prefer the sentiment-oriented mode to others. The average satisfaction level of system is 4.2 (out of 5). With this data, we hope to contribute more to the development and implementation in this field of study.
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Lee, Hou-Chun, and 李厚均. "Using Text Mining Techniques for E-Commerce WOM Analysis." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q57v27.

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碩士<br>國立中正大學<br>資訊管理系研究所<br>106<br>With the rapid development of information technology and the increasing use of the internet, many enterprises have started to develop e-commerce platform apps to provide consumers a marketplace to buy and sell goods. After users have used these apps, they give a review based on how the experience was for them. Browsing all these reviews is very time consuming and inefficient. Finding a method to quickly sort consumer reviews to understand user sentiment is a very important research topic. In view of this, this study uses chance discovery and text mining to analyze reviews of the apps Yahoo! Auctions and Shopee from Google Play and understand the characteristics of e-commerce and user sentiment. This study cuts the data into periods of time to understand the relevance and differences between the various periods’ vocabulary usage, with the visualization tool KeyGraph to describe the correlation between the e-commerce data and vocabulary-emotions associations map. Using these results allows the understanding of the changes in the e-commerce data, to identify the potential future trends. This study found that although companies actively provide innovative functions such as cash flow system, live broadcast services, etc., which can bring benefits to the company, a number of factors such as systems, buyers and sellers need to be taken into account. For future research, in addition to e-commerce and user sentiments, other types of comments such as emotional language is worth studying.
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Chang, Hsiu-Yuan, and 張琇媛. "A Study of E-commerce Recommendation Systems Based on Sentiment Analysis: A Case Study of Mobile Phones." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/yh43vq.

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碩士<br>淡江大學<br>資訊管理學系碩士班<br>104<br>Due to the information explosion which causes the overload of information, the users have to continuously browse and search to get the information they preferred. Therefore, many scholars start to study information filtering to reduce the problem of information explosion and help user to select the items based on user’s preferences. Recommendation system is one of the use of the information filtering which can provide information or item based on user’s preferences, requirements and interests more efficiently and precisely. Currently, the recommendation methods can be categorized into the following three types: content-based, collaborative and hybrid recommendation methods. The hybrid recommendation method has been most widely used in the field of e-commerce. In order to meet the needs of the user when select products from the recommendation system, this study therefore presented the e-commerce recommendation system with sentiment analysis (ECRS-SA). By using automatic data extraction, opinion extraction, and polarity analysis to collected user’s usage records and comments from the social networks. Then rate the products according to the results of sentiment analysis and give recommendation to users. Finally, we compared ECRS-SA and ECRS-SA without Sentiment Analysis (ECRS) to evaluate user’s need and recommendation accuracy. In this paper, we used the survey of satisfaction and the recommendation system evaluated method to analyze user’s usage records. The experiment results show that eighty percent of people satisfy with the ECRS-SA and the F-Measure of the recommendation result is 70.48% higher than the ECRS by 15.75%. The ECRS-SA results is much match to user’s need and the accuracy rate is over ninety percent. And the user’s preference will be impacted by the ECRS-SA.
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Books on the topic ""Sentiment Analysis in E-Commerce: Techniques"

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Statistical and machine-learning data mining: Techniques for better predictive modeling and analysis of big data. 2nd ed. Taylor & Francis, 2012.

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Ratner, Bruce. Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. Taylor & Francis Group, 2013.

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Ratner, Bruce. Statistical and Machine-Learning Data Mining : : Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition. Taylor & Francis Group, 2017.

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Ratner, Bruce. Statistical and Machine-Learning Data Mining : : Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition. Taylor & Francis Group, 2017.

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Statistical and Machine-Learning Data Mining : : Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition. Taylor & Francis Group, 2017.

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Statistical and Machine-Learning Data Mining : : Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition. Taylor & Francis Group, 2017.

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Pachpande, Priti, and Sham Bachhav. Indian Business Case Studies Volume IV. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869401.001.0001.

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Abstract Case Volume IV brings industry live into the classroom for management students and onto the desks of scholars, managers, and entrepreneurs for analysis, prognosis, and application in the real world business issues through very relevant and interesting case studies from Indian Business and Economy. The case studies are chosen ones from every management field, be it Business strategy, Human Resources Management, Finance, Marketing or Business operations management aspects. The case studies are on major transformative events in business environment and strategy that brought in disruptive changes like e-commerce, digitalization in every sector, be it Telecom, Banking, Insurance, or public sector are included as case studies in this case volume. The case volume includes Business Case Studies designed and developed based on current business and economic scenario in India both based on published data and field search live case studies. These case studies enable effective adoption to case methodology of teaching for UG and PG Studies in Business Management as also best suited for Management Development programs in Business and Industry. These case studies offer best of opportunities and tools to be used in offline and online teaching and learning methodology especially help developing analytical skills and problem resolution techniques in the students of Business Management studies and young and aspiring business executives globally.
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Book chapters on the topic ""Sentiment Analysis in E-Commerce: Techniques"

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Calleo, Yuri, and Simone Di Zio. "Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.33.

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In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning data and information (reading of documents, literature review and consultation of experts) to understand more about the object of the foresight study. The daily use of social networks causes an exponential increase of data and for this reason here we deal with the problem of speeding up and optimizing the Scanning phase by applying a new combined method based on the analysis of tweets with the use of unsupervised classification models, text-mining and spatial data mining techniques. For the purpose of having a qualitative overview, we applied the bag-of-words model and a Sentiment Analysis with the Afinn and Vader algorithms. Then, in order to extrapolate the influence factors, and the relevant key factors (Kayser and Blind, 2017; 2020) the Latent Dirichlet Allocation (LDA) was used (Tong and Zhang, 2016). Furthermore, to acquire also spatial information we used spatial data mining technique to extract georeferenced data from which it was possible to analyse and obtain a geographic analysis of the data. To showcase our method, we provide an example using Covid-19 tweets (Uhl and Schiebel, 2017), upon which 5 topics and 6 key factors have been extracted. In the last instance, for each influence factor, a cartogram was created through the relative frequencies in order to have a spatial distribution of the users discussing each particular topic. The results fully answer the research objectives and the model used could be a new approach that can offer benefits in the scenario developments process.
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Patel, Astha, Ankit Chauhan, and Madhuri Vaghasia. "A Survey on E-Commerce Sentiment Analysis." In Expert Clouds and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2500-9_6.

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Praveen, M., R. R. Vijay, R. S. Aaditya Shreeram, and S. Manohar. "E-commerce Product Sentiment Assessment and Aspect Analysis." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82383-1_26.

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Sirajudeen, S., Balaganesh, Haleema, and V. Ajantha Devi. "Application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82322-1_14.

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Ibrahim, Waleed, Binaya Subedi, Sabreena Zoha, Abdussalam Ali, and Emran Salahuddin. "Comparative Analysis: Recommendation Techniques in E-Commerce." In Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_8.

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Arora, Sonakshi, P. Harika, and Sakshi Shringi. "Impact of Sentiment Analysis in E-Commerce and Cybersecurity." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73494-6_24.

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Chamekh, Ameni, Mariem Mahfoudh, and Germain Forestier. "Sentiment Analysis Based on Deep Learning in E-Commerce." In Knowledge Science, Engineering and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10986-7_40.

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Sirajudeen, S., Balaganesh, Haleema, and V. Ajantha Devi. "Correction to: Application of Ensemble Techniques Based Sentiment Analysis to Assess the Adoption Rate of E-Learning During Covid-19 Among the Spectrum of Learners." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82322-1_22.

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Bordoloi, Monali, and Saroj Kumar Biswas. "Graph-Based Sentiment Analysis Model for E-Commerce Websites’ Data." In Cognitive Informatics and Soft Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0617-4_45.

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Zheng, Lizhou, Peiquan Jin, Jie Zhao, and Lihua Yue. "Multi-dimensional Sentiment Analysis for Large-Scale E-commerce Reviews." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10085-2_41.

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Conference papers on the topic ""Sentiment Analysis in E-Commerce: Techniques"

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Nawrocka, Agata, Marcin Nawrocki, Andrzej Kot, and Marcin Słychan. "The Application of Machine Learning in Sentiment Analysis of Social Media and E-commerce: A Review of Methods and Techniques." In 2025 26th International Carpathian Control Conference (ICCC). IEEE, 2025. https://doi.org/10.1109/iccc65605.2025.11022793.

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Tirumanadham, NS Koti Mani Kumar, Thaiyalnayaki S, and Ganesan V. "Enhancing Student Performance Prediction using E-Learning through Multimodal Data Integration and Machine Learning Techniques." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933211.

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Yaqin, Ainul, and Ema Utami. "Comparison of Classification Algorithm In Sentiment Analysis of E-Commerce App Comments Based on Feature Extraction and Imbalanced Handling Technique." In 2024 7th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2024. https://doi.org/10.1109/icoiact64819.2024.10913244.

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Aydoğan, Elif Hanife, and Feyza Yildirim Okay. "Sentiment Analysis of Reviews for E-Commerce Applications." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773400.

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Noman, Abdullah Al, M. D. Uodoy Hossan Rafi, Prapti Saha Antara, M. B. Mahir Tanzim, Md Whahidul Islam Payel, and Md Hasan Imam Bijoy. "Predicting Customer Sentiment from Bangladeshi E-Commerce using Machine Learning Techniques." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013335.

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Paul, Priti, Soubhik Acharya, Bitan Misra, Soumi Majumder, Nilanjan Dey, and Priya Pise. "Sentiment Analysis for E-Commerce Product Reviews Using CNN-LSTM." In 2024 First International Conference for Women in Computing (InCoWoCo). IEEE, 2024. https://doi.org/10.1109/incowoco64194.2024.10863425.

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Ao, Xiqin, Liyang Zhao, Xingyu Liu, and Tingting Wu. "Sentiment Analysis of E-Commerce Product Reviews Based on BiLSTM Model." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743562.

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Sharma, Suchita, and Nishith Desai. "Improvising E-Commerce Sentiment Analysis with Hybrid VADER-BERT Ensemble Model." In 2024 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). IEEE, 2024. https://doi.org/10.1109/cenim64038.2024.10882816.

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He, Min. "A Review of Data Fusion and Deep Learning Models for Multimodal Sentiment Analysis." In International Conference on E-commerce and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013189700004568.

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Giri, Yuvraj, Vandana Bhagat, and Ashaq Hussain Ganie. "Empowering E-commerce: Leveraging Open AI and Sentiment Analysis for Smarter Recommendations." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739003.

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