Sentiment Analysis of E-commerce Customer Reviews Based on Natural Language Processing

Lin, Xiaoxin

doi:10.1145/3436286.3436293

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…In order to transition the traditional buying experience to e-commerce, NLP is mostly utilized for fashion advice and recommendations [34,78,79,124,126,[155][156][157][158][159][160][161][162][163][164][165][166][167][168]. Another significant area of study focuses on gathering data from consumers using sentiment analysis techniques, such as via reviews or social media [42,44,[169][170][171][172][173][174][175][176][177][178][179][180]. Finally, studies on picture tagging using textual content analysis have been conducted [55,61,73,89,94,95,98,104,107,113,116,118,[181][182]…”

Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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Section: Ai In B2c Retail and Application Areas Of The Techniquesmentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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“…Considering their study, Random Forest had the highest accuracy (90.66%) and Naïve Bayes had the lowest accuracy (54.84%). Similarly, Lin [33] experimented with XGBoost and LightGBM machine learning and various models (e.g., Support Vector Machine, Random Forest, and Logistic Regression) in the Women's clothing online market. LightGBM had the highest accuracy (98%).…”

Section: Machine Learning and User Experiencementioning

confidence: 99%

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Lee¹,

Lee²,

Kim³

2022

Computers, Materials &Amp; Continua

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Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of mobile applications. To overcome this limitation, several researchers perform user experience analysis through online reviews and star ratings. However, some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text. This variation disturbs the performance of machine learning. To alleviate the inconsistencies, Valence Aware Dictionary and sEntiment Reasoner (VADER), which is a sentiment classifier based on lexicon, is introduced. The current study aims to build a more accurate sentiment classifier based on machine learning with VADER. In this study, five sentiment classifiers are used, such as Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression, Light Gradient Boosting Machine (LightGBM), and Categorical boosting algorithm (Catboost) with three embedding methods (Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec). The results show that classifiers that apply VADER outperform those that do not apply VADER, excluding one classifier (Logistic Regression with Word2Vec). Moreover, LightGBM with TF-IDF has the highest accuracy 88.68% among other models.

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“…The research shows that while ensuring the prediction accuracy (equivalent to XGBoost algorithm), the resource occupation is greatly reduced and the running speed is improved significantly [9,10]. Lin et al [11] used the female clothing review data to study the performance of different algorithms in the personalized product recommendation task. The results show that LightGBMRanker model has higher accuracy than XGBoost algorithm.…”

Section: 1related Workmentioning

confidence: 99%

Product recommendation system based on LightGBMRanker algorithm

Liu

2022

Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022)

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With the continuous development of e-commerce websites, online shopping has become an indispensable part of life. However, the huge amount of product information makes customers lost and weakens the ability to obtain the expected information. In recent years, sorting algorithm has been applied in recommendation system to recommend information and products to customers according to product information and customer behavior patterns. Based on a large number of historical sales commodity data and customer data, combined with LightGBMRanker algorithm and feature engineering processing, this paper establishes a commodity recommendation and sorting model. And judge the accuracy of the sorting model according to the subsequent purchase data of customers. The results show that our model can effectively provide customers with desired product recommendations, and the prediction accuracy is higher than other algorithms. Specifically, the maximum map@12 value of our model is 0.0284, which is 26.22% and 22.41% higher than SVM algorithm and LSTM algorithm respectively. In addition, the ranking of important features that affect product sorting and recommendation is given in this paper, and some constructive guidance is obtained.

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Sentiment Analysis of E-commerce Customer Reviews Based on Natural Language Processing

Cited by 14 publications

References 12 publications

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Product recommendation system based on LightGBMRanker algorithm

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