Product Review Data Research Articles

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Abstract Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.

Extracting design knowledge from online product reviews to support design creativity

ABSTRACT E-commerce has made customer-generated product reviews accessible for analysis. Each customer review summarizes the product's unique customer experience and features. In product development processes, it is essential to reach the information content of customer reviews early. Using customer content as data provides designers with comprehensive, cost-effective, and rapid insights into product evaluation, user experience, and even identification of new product features and creative design interventions. Despite all these superior potentials, analyzing unstructured and mass product review data takes time and effort with conventional methods. This study proposes to utilize product reviews as input for developing user experience-based products and to support designers' creativity in the early design phases. Therefore, in this study, the opinions of consumers about a product were subjected to a text mining analysis at different levels, and mood values were determined for each category and sub-product aspect with their justification. In the last part of the study, the essential product improvement areas based on the comment/sentiment score are presented, and the potential of product comments as information content about design creativity is discussed.

Exploring the Effectiveness of Deep Learning in Analyzing Review Sentiment

This study aimed to analyze sentiment in office product reviews by using word embedding with three neural network modeling approaches: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Office product review data is taken from Amazon's reviews of office products covering a wide range of sentiments. Word embedding converts text into a numerical vector representation for neural network processing. Experimental comparison of this model reveals that CNN achieves the highest accuracy, 77.99%. The CNN model effectively extracts significant features from review text, improving sentiment classification performance. Although the LSTM and GRU models show satisfactory results, they do not match CNN performance. These findings demonstrate the effectiveness of word embedding and neural networks for sentiment analysis in office product reviews. This provides valuable insights for companies to improve their products based on user feedback from online reviews. Additionally, this research serves as a foundation for further advances in sentiment analysis across a wide range of other products and services

INVESTIGATION OF CUSTOMER PREFERENCE CHANGES FOLLOWING COVID-19 MARKET DISRUPTION USING ONLINE REVIEW ANALYSIS

AbstractCOVID-19 pandemic has continued to pose a challenge to the society for almost three years, adversely affecting all segments of population in a scale unseen in the recent decades. Over the course of COVID-19 pandemic, many people have lost their jobs and income. These social and economic impacts have disrupted the market, potentially altering people's attitudes towards different product features. Therefore, this paper investigates the changes in customer preferences on various features of different products, before and after COVID-19 pandemic, using online review analysis. The proposed framework consists of four stages. Firstly, product review data is collected and preprocessed. Secondly, customer interest in product features is explored using latent Dirichlet allocation. Thirdly, customer sentiment for these features is analyzed with Valence Aware Dictionary and sEntiment Reasoner. Finally, the importance of each feature is calculated based on interpretable machine learning. The proposed method is tested on two real-world datasets – smartphone and laptop reviews. The result reveals the changes in customer sentiments and preferences for product features, thus helping companies quickly establish strategies in rapidly changing market environments.

A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.

Sentiment Analysis Tool for Amazon Product Reviews

Abstract: Sentiment analysis has become a vital componentof modern data analysis, particularly for businesses that relyon customer input to improve their products and services. We employ Natural Language Processing (NLP) techniques to analyze sentiment in Amazon product reviews in this study. Our major goal is to categorize the assessments based on whether they are good, negative, or neutral. We’ll use Amazon product review data, which includes a large number of reviews from various categories, such as books, electronics, and clothes.

Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service

Because of the rapid increase of data in the cloud of Amazon Web Service (AWS), the traditional methods for analyzing this data are not good and inappropriate, so unconventional methods of analysis have been proposed by many data scientists such as concurrent/ parallel techniques to meeting the requirements of performance and scalability entailed in such big data analyses. In this paper we are used Hadoop Map Reduce system that contains Hadoop Distributed File System (HDFS) and Hadoop cluster. We optimized it by combining it with five efficient Data Mining (DM) algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Correlative Naïve Bayes classifier (CNB), and Fuzzy CNB (FCNB) for strong analytics of cloud big data. The proposed system applied on product review data that taken form the cloud of AWS. The Evaluation of Hadoop Map Reduce done with important benchmarks as Mean Absolute Percentage Error (MPAE), Root Mean Square Error (RMSE), and runtime for word count, sort, inverted index. Also, the evaluation of DM models with Hadoop Map Reduce system done by using accuracy, sensitivity, specificity, memory, and running time. Experiments have shown that FCNB is effective in addressing the problem of big data.

Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.

Model Comparison in Sentiment Analysis: A Case Study of Amazon Product Reviews

Sentiment analysis is essential in NLP, especially in businesses because it can improve customer services. This paper focuses on a particular case of sentiment analysis, a case study of Amazon reviews of books on kindle. Firstly, this paper applies several non-deep-learning algorithms including Logistic Regression, Naïve Bayes, Support Vector Machine, Convolutional Neural Network, and Recurrent Neural Network, and compares their accuracies. Especially, for deep learning methods, this paper studies the slope of accuracies concerning the number of hidden layers. Secondly, as a multi-class text classification problem, the product review data set has five labels ranging from one star to five stars, a new method called Hybrid Sequential Binary Classification (HSBC) is introduced in this paper, which improves the behavior of classical binary classifiers on a multi-class text classification problem. Moreover, a comparison of HSBC and multi-class classification models is presented.

A Comparison of Opinion Mining Algorithms by Using Product Review Data

A Comparison of Opinion Mining Algorithms by Using Product Review Data

Reasonable Setting Values for Anonymization Algorithms for Online Educational Data Analysis Support System

We propose a criterion of reasonable parameters for an algorithm that aggregates target data with anonymized data for safe data analysis in online educational systems, called learning management systems (LMSs). We also statistically investigate parameters that can satisfy the proposed criteria using an anonymization algorithm and real large-scale data. We use an open dataset containing one year's worth of product review data due to the difficulty of collecting LMS data large enough for the evaluation. Furthermore, we discuss an approach to address cases where no parameter satisfies the proposed criteria.

Sentiment Analysis of Beauty Product Reviews Using the K-Nearest Neighbor (KNN) and TF-IDF Methods with Chi-Square Feature Selection

The rise of beauty products in recent times can make consumers hesitate to choose a beauty product, especially for women. Beauty product reviews have become a very valuable source of information for consumers in making decisions to purchase a product in improving their products and marketing strategies. The process of sentiment analysis on negative and positive beauty product reviews will be classified one by one. Therefore, in this study, sentiment analysis was applied to the beauty product review data using the K-Nearest Neighbor (KNN) method to find the best k in the case of this study. The dataset used will be pre-processed with case folding, noise removal, tokenization, stemming, stopword removal, and slang words, for feature extraction using Term Frequency Inverse Document Frequency (TF-IDF) to calculate the weight of a word in the document, and The feature selection method uses Chi-Square which aims to select the features needed to increase the accuracy value. In this study, the best accuracy value was 71% of the data classified using KNN with a k value of 50 and the model on feature selection with 76 features.

Competitive Product Identification and Sales Forecast Based on Consumer Reviews

Sellers readily obtain consumer product evaluations from online reviews in order to identify competitive products in detail and predict sales. Firstly, we collect product review data from shopping websites, social media, product communities, and other online platforms to identify product competitors with the help of word-frequency cooccurrence technology. We take mobile phones as an example to mine and analyze product competition information. Then, we calculate the product review quantity, review emotion value, product-network heat, and price statistics and establish the regression model of online product review forecasts. In addition, the neural-network model is established to suggest that the relationships among factors are linear. On the basis of analyzing and discussing the impact of product sales of the competitors, product price, the emotional value of the reviews, and product-network popularity, we construct the sales forecast model. Finally, to verify the validity of the factor analysis affecting the sales and the rationality of the established model, actual sales data are used to further analyze and verify the model, showing that the model is reasonable and effective.

Comment information extraction based on LSTM and Neural Networks

With the advent of the era of big data, the amount of data has also increased geometrically. People’s ability to obtain effective information has gradually declined. At present, most e-commerce platforms only focus on the sentiment analysis of positive and negative reviews. It is difficult for users and businesses to extract user opinions and views from the massive review data. For the product review data of a certain hard disk, use the LSTM model to train the sentiment classification model. Finally, the neural network is used to find the keywords of the comment data and the word cloud diagram is used to display the analysis results. Through the research, it can be found that LSTM emotion classifier can classify comments with high accuracy and words closely related to comment emotion tendency can be found according to the weight of neural network.

Online Recommender System

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

Personal customized recommendation system reflecting purchase criteria and product reviews sentiment analysis

As the size of the e-commerce market grows, the consequences of it are appearing throughout society. The business environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the user's subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user.

Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features

Fraudulent online sellers often collude with reviewers to garner fake reviews for their products. This act undermines the trust of buyers in product reviews, and potentially reduces the effectiveness of online markets. Being able to accurately detect fake reviews is, therefore, critical. In this study, we investigate several preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to build a fake review detection system. Given the nature of product review data, where the number of fake reviews is far less than that of genuine reviews, we look into the results of each class in detail in addition to the overall results. We recognise from our preliminary analysis that, owing to imbalanced data, there is a high imbalance between the accuracies for different classes (e.g., 1.3% for the fake review class and 99.7% for the genuine review class), despite the overall accuracy looking promising (around 89.7%). We propose two dynamic random sampling techniques that are possible for textual-based featuring methods to solve this class imbalance problem. Our results indicate that both sampling techniques can improve the accuracy of the fake review class—for balanced datasets, the accuracies can be improved to a maximum of 84.5% and 75.6% for random under and over-sampling, respectively. However, the accuracies for genuine reviews decrease to 75% and 58.8% for random under and over-sampling, respectively. We also discover that, for smaller datasets, the Adaptive Boosting ensemble model outperforms other single classifiers; whereas, for larger datasets, the performance improvement from ensemble models is insignificant compared to the best results obtained by single classifiers.

Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-Generated Content

Although product design has been considered as a collaborative activity, progress in developing methods to facilitate the design process has been hampered by difficulties in building common bases among various stakeholders. Customers may only have general needs of the product in layman's terms instead of the sufficient domain knowledge to identify the product specifications. Thus, there is a great need in design research to translate the expressed needs in natural language to design specifications and bridge this semantic gap. By leveraging the massive amount of online user-generated content, we develop a deep learning-based method to automatically identify product design parameters or product specifications. Specifically, we crawl product review data and the corresponding product metadata from e-commerce websites. A convolutional neural network based solution is provided to map product reviews to product specifications. Experimental results show that the method can be well adapted to the mapping from customer needs in the natural language to product specifications. The method facilitates product design by addressing the semantic gap between general customer needs and detailed product specifications. In addition, the automated mapping greatly improves the efficiency and reduces labor in the design process.

Social Media Relevance Filtering Using Perplexity-Based Positive-Unlabelled Learning

Internet user-generated data, like Twitter, offers data scientists a public real-time data source that can provide insights, supplementing traditional data. However, identifying relevant data for such analyses can be time-consuming. In this paper, we introduce our Perplexity variant of Positive-Unlabelled Learning (PPUL) framework as a means to perform social media relevance filtering. We note that this task is particularly well suited to a PU Learning approach. We demonstrate how perplexity can identify candidate examples of the negative class, using language models. To learn such models, we experiment with both statistical methods and a Variational Autoencoder. Our PPUL method generally outperforms strong PU Learning baselines, which we demonstrate on five different data sets: the Hazardous Product Review data set, two well known social media data sets, and two real case studies in relevance filtering. All datasets have manual annotations for evaluation, and, in each case, PPUL attains state-of-the-art performance, with gains ranging from 4 to 17% improvement over competitive baselines. We show that the PPUL framework is effective when the amount of positive annotated data is small, and it is appropriate for both content that is triggered by an event and a general topic of interest.

Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification.

Cross-lingual sentiment classification (CLSC) aims to leverage rich-labeled resources in the source language to improve prediction models of a resource-scarce domain in the target language. Existing feature representation learning-based approaches try to minimize the difference of latent features between different domains by exact alignment, which is achieved by either one-to-one topic alignment or matrix projection. Exact alignment, however, restricts the representation flexibility and further degrades the model performances on CLSC tasks if the distribution difference between two language domains is large. On the other hand, most previous studies proposed document-level models or ignored sentiment polarities of topics that might lead to insufficient learning of latent features. To solve the abovementioned problems, we propose a coarse alignment mechanism to enhance the model's representation by a group-to-group topic alignment into an aspect-level fine-grained model. First, we propose an unsupervised aspect, opinion, and sentiment unification model (AOS), which trimodels aspects, opinions, and sentiments of reviews from different domains and helps capture more accurate latent feature representation by a coarse alignment mechanism. To further boost AOS, we propose ps-AOS, a partial supervised AOS model, in which labeled source language data help minimize the difference of feature representations between two language domains with the help of logistics regression. Finally, an expectation-maximization framework with Gibbs sampling is then proposed to optimize our model. Extensive experiments on various multilingual product review data sets show that ps-AOS significantly outperforms various kinds of state-of-the-art baselines.