What affects customers online shopping behavior, research that applied machine learning to Amazon product reviews

The application of Natural Language Processing (NLP) in marketing has undergone significant evolution, with machine learning algorithms playing a crucial role in extracting valuable insights from complex textual data. This study focuses on comparing the performance of Support Vector Machine (SVM), Random Forest (RF), Nave Bayes (NB), and a specialized sentiment analysis model, Latent Dirichlet Allocation (LDA), in the context of online platform reviews. While previous research has delved into individual algorithms, there is a paucity of horizontal comparisons. Suitable algorithms for sentiment analysis on online platform reviews, specifically for Amazon, were filtered in this work. A dataset from Kaggle (https://www.kaggle.com/datasets/arhamrumi/amazon-product-reviews) comprising 500,000 reviews and 10 columns was utilized, overcoming time and resource constraints by opting for secondary data analysis. The primary objective was to assess the performance metrics of SVM, RF, NB, and LDA in classifying reviews into positive, neutral, and negative sentiments. Despite the massive size of the dataset posing challenges to the accuracy of the algorithms, nuanced results in precision, recall, and F-score were observed, not replicated in prior studies. Attempts to enhance accuracy by switching vectorizers yielded marginal improvements. Interestingly, LDA emerged as a transformative model, leveraging its ability to generate WordClouds for a systematic analysis of customers' emotional attachments. In addition to sentiment analysis, an investigation into the identification of factors influencing consumer purchasing behavior on Amazon was conducted. By training the LDA model on positive, neutral, and negative comments, distinctive features associated with each sentiment category were extracted. This analysis aims to unravel the underlying product features that contribute significantly to customer decision-making processes. In conclusion, this work provides a comprehensive evaluation of SVM, RF, NB, and LDA in the realm of sentiment analysis on Amazon product reviews. The findings shed light on the challenges posed by large datasets, the limitations of traditional vectorizers, and the unique capabilities of LDA in uncovering emotional nuances. Moreover, the investigation into consumer purchasing behavior offers valuable insights for marketers seeking to understand the factors influencing online shopping decisions.