LSTM-based Customer Preference Identification and Prediction in Customer Online Reviews
Abstract
With the development of internet platforms, consumers increasingly rely on online product reviews during the shopping process. Customer online reviews can guide product design and provide valuable insights for businesses. Therefore, analyzing customer online reviews has become a research hotspot. However, existing studies suffer from some limitations, including the lack of fine-grained sentiment analysis and word clustering analysis. To address these issues, this study proposes a method based on Long Short-Term Memory (LSTM) for identifying and predicting customer preferences, aiming to provide more accurate information support for product design improvement personnel. This research first conducts data collection, preprocessing, and word vector computation. Subsequently, dictionary and sentiment labels are extracted. Based on a topic clustering system, sentiment labels are quantified for polarity classification, and sentiment scores and importance values are calculated as evaluation metrics for customer preference data. The preference-identified data are then applied to an LSTM prediction model, and the dataset is partitioned accordingly. Finally, the trained model is used for LSTM-based prediction of importance values and sentiment scores. The research results demonstrate that this method can accurately predict product preferences based on dynamic online reviews.
This paper was recommended by Regional Editor Tongquan Wei.
