User behavior analysis on e-commerce using NLP techniques

This study presents an in-depth investigation into the potential for sentiment analysis (SA) and machine learning (ML) in facilitating the sales prediction and customer retention processes for both small and large-scale businesses. Online platforms including blogs, social networks, and review portals have transformed the marketing landscape, enabling consumers to voice their opinions on a vast array of topics, from product reviews to popular culture. These digital platforms not only foster customer engagement, but also provide businesses with an invaluable data source for predictive analysis, essential in strategic sales forecasting and customer relationship management. In this study, we compiled a comprehensive dataset of product review tweets, serving as a rich representation of consumer sentiment. To ensure the integrity and relevance of the data, we engaged in rigorous preprocessing methodologies, mitigating potential noise and inconsistencies. Following the cleaning phase, we utilized the Valence Aware Dictionary and sEntiment Reasoner (VADER), a lexicon and rule-based sentiment analysis tool, in conjunction with several machine learning algorithms. The objective was to ascertain the most effective means of classifying the sentiment polarity of product reviews, subsequently aiding in sales prediction. Our findings reveal that while VADER offers notable benefits in sentiment analysis, ML techniques present superior accuracy in classifying the polarity of product reviews. More specifically, logistic regression (LR) was found to be the top-performing algorithm in this context. Across a multitude of evaluation metrics, including accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve (AUC), LR consistently outperformed its ii counterparts, thus solidifying its position as the most apt choice for sentiment classification in product reviews. Notably, other algorithms such as XGBoost and Stochastic Gradient Descent (SGD) also demonstrated competitive performance. They can serve as plausible alternatives in situations where model interpretability is not the prime concern and a higher degree of model complexity is permissible. These findings contribute to an emerging body of knowledge, illuminating the potential of SA and ML in providing businesses with robust tools for understanding customer sentiment, predicting sales, and consequently enhancing customer retention strategies. The implications of this study extend beyond academia, promising substantial real-world benefits for various stakeholders in the business sphere.