Sentiment Analysis on Product Reviews from Shopee  Marketplace using the Naïve Bayes Classifier

Kaburuan, Emil Robert; Sari, Yunita Sartika; Agustina, Ika

doi:10.24843/lkjiti.2022.v13.i03.p02

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Data was collected using the web crawling method from reviews written by users who purchased one of the women's home clothing products or house dresses sold on the Shopee marketplace. The accuracy obtained reached 90.03% with a total dataset of 2907 data (Kaburuan et al, 2022).…”

Section: Product Review On Shopeementioning

confidence: 93%

“…The limitation of the problem in this research is that the dataset used is product reviews on Shopee, totaling 11,318 data in English and divided into two classes of positive and negative sentiment. The accuracy value with supervised Delta TF-IDF was 88.5% and with unsupervised TF-IDF the accuracy value was 87.9% (Kaburuan et al, 2022) Naïve Bayes Review of one of the women's home clothing products or home dresses sold on Shopee…”

Section: Introductionmentioning

confidence: 99%

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Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Harmandini,

2024

SinkrOn

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Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.

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Section: Product Review On Shopeementioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Harmandini,

2024

SinkrOn

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“…The main processes of preprocessing include removing irrelevant words, handling duplicates to ensure data integrity, normalizing data to a consistent format, handling outliers to prevent skewed analysis, and performing feature extraction to reveal underlying patterns in the dataset [20] [21] [22]. By performing these preprocessing steps, researchers create a cleaner, more standardized dataset that becomes the basis for accurate and in-depth data analysis.…”

Section: B Processing Data 1) Preprocessingmentioning

confidence: 99%

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Abdul Fadlil,

Riadi,

Andrianto

2024

IJCDS

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The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of 93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and 93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the most optimal results in the context of online marketplace sentiment analysis using data from Twitter

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“…Topic analysis can be developed in more depth by looking for topics that negatively and positively influence services or products sold in an online shop. Meanwhile, sentiment analysis can be used as a classification method to analyze negative and positive influences on text data [7]. Classification methods of sentiment analysis and topic analysis are combined, and we will get topics with a positive or negative trend regarding the comments given [8] by online shop customers.…”

Section: Introductionmentioning

confidence: 99%

Topic Sentiment Using Logistic Regression and Latent Dirichlet Allocation as a Customer Satisfaction Analysis Model

Cahyo,

Aesyi,

Santosa

2024

J.INFOTEL

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Buying and selling goods now is more interesting through e-commerce or marketplaces because of the ease of carrying out online transactions. Each transaction usually generates a response from the customer. The transaction response on the Shopee platform is still in paragraph form and needs to be more specific. Therefore, this research aims to build a model analysis of customer satisfaction using the best algorithm between support vector machine (SVM), random forest, and logistic regression. This research method uses sentiment classification with logistic regression because the logistic regression algorithm has the best accuracy, with an accuracy of 90.5. Meanwhile, the SVM algorithm achieved an accuracy of 90.4, and random forest reached 90.2. The three algorithms were tested three times, splitting data train:test at 80:20, 70:30, and 60:40. The best results were obtained by splitting data at 60:40. The best model is used to predict data without labels. The prediction produces 12,844 positive sentiment comment data, 112 negative sentiment comment data, and 70 neutral sentiment comment data. The results of this research continued to topic modeling using latent dirichlet allocation (LDA) to generate a trending topic of customer satisfaction on sales products. Implications of discussing each trend topic can be used as a reference for improving products and services, especially in communicating with customers.

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Sentiment Analysis on Product Reviews from Shopee Marketplace using the Naïve Bayes Classifier

Cited by 6 publications

References 6 publications

Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning

Topic Sentiment Using Logistic Regression and Latent Dirichlet Allocation as a Customer Satisfaction Analysis Model

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