Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors

Consumer reviews are an important source of data used to judge and examine consumer sentiment, and data mining for reviews of electronic products is an important way to help improve the design of electronic products. The research is based on the consumer reviews of online cell phone e-commerce, The paper constructs a sentiment dictionary in this field based on the Sentiment Oriented Point Mutual Information (SO-PMI) algorithm, and the sentiment weight of the review word vectors. An extreme Gradient Boosting Tree (XGBoost) is used to integrate word vectors and a Large Language Model (LLM) to construct a sentiment recognition model, and finally, a review sentiment index is derived, which unfolds from multiple dimensions to analyze the sentiment tendency in consumer reviews. The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. When applying the weighted word vector method, the model has been significantly improved compared with the LLM model, the accuracy is increased by 5%, the accuracy is increased by 10%, and the comprehensive accuracy is increased by 2% after the comprehensive application of the two.