基于深度学习的电商评论情感分析——以手机电商评论为例

Sentiment Analysis of E-Commerce Reviews Based on Deep Learning—Taking Mobile E-Commerce Reviews as an Example

摘要: 本文基于深度学习方法和LDA主题模型，对电商平台手机产品的用户评论进行情感分析与主题挖掘。针对用户发表的繁杂多样且蕴含多方面体验信息的商品评论，本研究借助BERT模型进行细粒度情感分析，并结合LDA主题模型提取消费者关注的核心主题。以手机评论数据为例，系统分析了消费者情感倾向分布及其对产品不同主题的重视程度，深入挖掘用户在性能、外观、服务体验等方面的核心诉求。为用户提供了一种快速洞察产品整体口碑态势的高效途径，也为手机厂商的产品优化、市场定位及营销策略制定提供了切实的数据支撑和决策依据。

Abstract: Based on deep learning method and LDA topic model, this paper conducts sentiment analysis and topic mining on user reviews of mobile phone products on e-commerce platform. Aiming at the complex and diverse product reviews published by users and containing various experience information, this study uses the BERT model for fine-grained sentiment analysis, and combines the LDA topic model to extract the core topics that consumers pay attention to. Taking mobile phone comment data as an example, this paper systematically analyzes the distribution of consumers’ emotional tendencies and their emphasis on different themes of products, and deeply explores the core demands of users in terms of performance, appearance and service experience. It provides a way for users to quickly understand the word-of-mouth of products, and also provides practical data support and decision-making basis for product optimization, market positioning and marketing strategy formulation of mobile phone manufacturers.