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In this work, we evaluated the efficacy of Google’s Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon’s fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks.

Understanding customer sentiment through product reviews is important for businesses aiming to enhance their products and services. Traditional sentiment analysis often struggles to detect the wide range of emotional nuances, particularly those expressed through emojis, in online reviews. To address this challenge, our paper introduces a novel technique called Emoji-Enhanced BERT (EEBERT) based, on the BERT architecture. The Sentiment Adjustment Factor (SAF), created for emoji tokenization with the embeddings layer, is applied to analyze sentiment and emotion within review text content. Sentiments classification is evaluated using three different methods: (1) evaluating the full review text; (2) analyzing the text with emojis; and (3) integrating star ratings and total votes in EEBERT. These methods were fine-tuned using a labeled Amazon product reviews dataset. The approach utilizes a well-vetted dataset comprising approximately 10 million customer reviews from four different categories. This dataset is condensed to 26,818 reviews to only consider emoji contained reviews, covering 3,275 distinct emojis. A systematic approach to classifying reviews and an innovative technique for assessing the emotional semantics of emojis are developed. The proposed model achieves an accuracy of 97.00%, further applies continuous testing via 5-fold cross-validation, achieving an average accuracy of 99.21%, which supports the reliability of the EEBERT model.

The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers’ perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women’s clothes retailer.

As technology advances, Facebook, Twitter, and microblogging sites have become the most effective platforms for communication and information exchange. Through these forums, people can share their views and experiences. These platforms enable discussion about a certain product that can be a valuable resource used to inform any decision-making process. For such studies, the majority of advanced-level researchers employed deep learning and machine learning models in conjunction with natural language processing (NLP). In recent years, the use of pre-trained models, such as Glove and BERT, in aspect-based sentiment analysis (ABSA) has increased. In ABSA, the auxiliary information is required to distinguish each aspect of this fine-grained task. However, BERT’s input format is restricted to a collection of words that cannot include more context knowledge. To address this problem, a BiLSTM and embedded CNN-based deep learning model has been presented for sentiment analysis at the aspect level. Initially, datasets were compiled from several sources. Then, an auxiliary feature was extracted using standard NLP. The auxiliary features were further refined and transformed into feature vectors based on the proposed embedded CNN model. Finally, a BiLSTM-based classification has been performed for sentiment classification. The experimental evaluation demonstrated that the performance of the suggested technique achieves on the SemEval dataset in terms of F1 score and accuracy by 81.7 and 83.3 percentage points, respectively, and on the product review dataset by 80.8 and 83.1 percentage points, respectively.

Driven by the rapid development of Internet, more e-commerce product reviews are available on e-commerce platforms, which can help enterprises make business decisions. Currently, bidirectional encoder representations from transformers (BERT) applied in the embedding layer contributes to achieve promising results in English text sentiment analysis (SA). This paper proposes a novel model Chinese BERT with fused deep neural networks (CBERT-FDNN), extracting richer and more accurate semantic and grammatical information in Chinese text. First, Chinese BERT with whole word masking (Chinese-BERT-wwm) is used in the embedding layer to generate dynamic sentence representation vectors. It is a Chinese pre-training model based on the whole word masking (WWM) technology, which is more effective for Chinese text contextual embedding. Second, multi-channel and multi-scale convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are designed to capture further crucial features in the feature extraction layer. To obtain more comprehensive sentence attributes, these features are concatenated together. Last, the model is evaluated on 100,000 sentence-level Chinese e-commerce product reviews for sentiment binary classification. The accuracy and F1 score can achieve 94.37% and 94.34%, respectively. Compared with the baseline models, the experiments show that our proposed model has higher accuracy and better prediction performance.

This study is entitled "The Influence of Price Perception, Product Reviews, and Ease of Purchasing Decisions on E-Commerce Tokopedia in Madiun City." This study aimed to determine the effect of price perceptions, product reviews, and convenience on purchasing decisions on e-commerce Tokopedia in Madiun City. This quantitative research uses a sampling technique using probability sampling with a simple random sampling technique. The respondents of this study were 384 people—data analysis using multiple linear regression using the SmartPLS 3.0 application. Based on the results of hypothesis testing, there is a positive and significant effect between price perceptions and purchasing decisions, a positive and significant effect between product reviews and purchasing decisions, and a positive and significant influence between convenience and purchasing decisions. However, the perception of price, product reviews, and convenience do not simultaneously affect purchasing decisions.

Purpose: The goal of this study is to discover how the Shopee Mojokerto market's consumers make purchasing decisions in relation to product ratings, convenience, trust, and pricing. Design/methodology/approach: A good illustration of quantitative research is this. A purposive sampling technique was used to interview 100 respondents for this study's sample. The data analysis approach employs multiple linear regression analysis, t-tests, and F-tests. SPSS 21 is used to do the computations. Findings: The test results reveal that in the Shopee market in Mojokerto, convenience, trust, and price have a favorable and considerable influence on purchasing decisions, but product dimensions have minimal effect. The primary factor influencing Mojokerto Shopee purchases is easy. Paper type: Research paper

The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models.

Nowadays, digital reviews and ratings of E-commerce platforms provide a better way for consumers to buy the products. E-commerce giants like Amazon, Flipkart, etc provide customers with a forum to share their experience and provide potential consumers with true evidence of the product's outcomes. To obtain useful insights from a broad collection of reviews, it is important to separate reviews into positive and negative feelings. In the proposed work, Sentiment Analysis is to be done on the consumer review data and categorize into positive and negative feelings. Naïve Bayes Classification, LSTM and Support Vector Machine (SVM) were employed for the classification of reviews from the various classification models. Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. In this research work, BERT Base Uncased model which is a powerful Deep Learning Model is presented to elucidate the issue of Sentiment Analysis. The BERT model gave an improved performance with good prediction and high accuracy compared to the other methods of Machine Learning in the experimental evaluation.

Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers’ product experience and contain a lot of important information, but inevitably there are malicious or irrelevant reviews. It is especially important to discover and identify the real sentiment tendency in online reviews in a timely manner. Therefore, a deep learning-based real online consumer sentiment classification model is proposed. First, the mapping relationship between online reviews of goods and sentiment features is established based on expert knowledge and using fuzzy mathematics, thus mapping the high-dimensional original text data into a continuous low-dimensional space. Secondly, after obtaining local contextual features using convolutional operations, the long-term dependencies between features are fully considered by a bidirectional long- and short-term memory network. Then, the degree of contribution of different words to the text is considered by introducing an attention mechanism, and a regular term constraint is introduced in the objective function. The experimental results show that the proposed convolutional attention–long and short-term memory network (CA–LSTM) model has a higher test accuracy of 83.3% compared with other models, indicating that the model has better classification performance.

In recent years, with the development of e-commerce, the scale of comment data has shown an exponential growth trend. In this paper, a product review hot spot discovery algorithm based on MapReduce-PR-HD is proposed. The algorithm uses the Vector Space Model to vectorize the text data of the reviews, and utilize the TF-IDF algorithm to calculate the position weight of the feature words, then combines the Canopy algorithm and the K-Means algorithm to achieve the hot spot discovery of product reviews. At the same time, the algorithm obtain the ability to process massive data through the MapReduce framework. Experiments demonstrate that the PR-HD algorithm has high accuracy and parallel efficiency. This allows product developers to obtain more direct and effective suggestions and feedback, which allows product developers to obtain more direct and effective suggestions and feedback.

In today’s society, sentiment analysis has gained due importance as it provides useful information about products that areused by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at anearly stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole.Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of thedetailed components based products and their associated features, a novel method is desired to meet these criteria. So far,no such method is explored that analyses the product’s components and their features simultaneously, on the basis ofsentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis ofproduct reviews while formulating a tree structure of product, components, and associated features. In addition, evaluationof double negative sentences, detecting questions and emotions from the review sentences are measured which increasesefficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.

Sentiment analysis is the process of identifying the idea of a text. People share the comments on social media stating their knowledge of the event and would like to know if most other people had a good or bad experience at the same event. This distinction can be made through Emotional-Analysis. Sentiment analysis captures informal text comments, posts and images from all comments shared by different users and classifies comments into different categories as neutral, negative or positive. This is also called as polarity separation. Various different types of ML and in-depth learning methods may be utilised in Sentiment Analysis like Support Vector Machines, NB, Haar Cascade, LBPH, CNN, etc. Emerging rise in popularity in Social Media has established a trend of posting images in restaurants to express their opinion on the food, ambience, etc which can be a useful resource to obtain opinion and feedback from the Customers. In this paper, the implementation of Sentiment Analysis on images containing users along with their faces from the restaurants review revealing it more efficacious in classifying and identifying sentiments of review-images.

Online product reviews sentiment classification plays an important role on service recommendation, yet most of current researches on it only focus on single-modal information ignoring the complementary information, that results in unsatisfied accuracy of sentiment classification. This paper proposes a cross-modal hypergraph model to capture textual information and sentimental information simultaneously for sentiment classification of reviews. Furthermore, a mixture model by coupling the latent Dirichlet allocation topic model with the proposed cross-modal hypergraph is designed to mitigate the ambiguity of some specific words, which may express opposite polarity in different contexts. Experiments are carried out on four-domain data sets (books, DVD, electronics, and kitchen) to evaluate the proposed approaches by comparison with lexicon-based method, Naïve Bayes, maximum entropy, and support vector machine. Results demonstrate that our schemes outperform the baseline methods in sentiment classification accuracy.

Tokopedia is one of the most popular e-commerce sites in Indonesia that offers consumer products from various categories. In each product section, a review feature is offered. This review feature became essential in evaluating the sellers and become one consideration for customers in making purchase consideration. Sentiment analysis of Tokopedia product reviews may provide the opportunity to look on how Tokopedia customers respond to product quality and sellers’ hospitality. In evaluating the model, the reviews were grouped as: “positive sentiment” and “negative sentiment” using the Random Forest method and 10-fold cross-validation. Data labelling was carried out automatically by calculating the sentiment score using Lexicon-Based. Visualization of the labelling results was then done using a bar graph and a word cloud on each class of sentiment in order to look up for information that is considered important and most discussed. The test results showed that the accuracy of the Random Forest Method with parameter mtry = 73 and ntree = 50 is 97.38% which leads to the conclusion that the Random Forest Method could well predict the product reviews of Tokopedia. The greater the accuracy, the better performance of the classification model.

The growth of ecommerce has triggered online reviews as a rich source of product information. Revealing consumer sentiment from the reviews through Sentiment Analysis (SA) is an important task of online product review analysis. Two popular approaches of SA are the supervised approach and the lexicon-based approach. In supervised approach, the employed machine learning (ML) algorithm is not the only one to influence the results of SA. The utilized text features also handle an important role in determining the performance of SA tasks. In this regard, we proposed a method to extract text features that takes into account semantic of words. We argue that this semantic feature is capable of augmenting the results of supervised SA tasks compared to commonly utilized features, i.e., bag-of-words (BoW). To extract the features, we assigned the correct sense of the word in reviewing the sentence by adopting a Word Sense Disambiguation (WSD) technique. Several WordNet similarity algorithms were involved, and correct sentiment values were assigned to words. Accordingly, we generated text features for product review documents. To evaluate the performance of our text features in the supervised approach, we conducted experiments using several ML algorithms and feature selection methods. The results of the experiments using 10-fold cross-validation indicated that our proposed semantic features favorably increased the performance of SA by 10.9%, 9.2%, and 10.6% of precision, recall, and F-Measure, respectively, compared with baseline methods.