Sentiment Analysis and Network Marketing Strategy Research Based on Online Reviews of Movie Consumers

在互联网时代，电影评论成为消费者表达观影感受的重要途径，也为电影行业的创作及网络营销提供了丰富的数据资源。随着电影市场的竞争加剧，如何有效利用消费者在线评论中的情感信息，制定精准的网络营销策略成为电影行业关注的焦点。本文以豆瓣网站Top 250榜单中前五部电影的评论文本为研究对象，运用Python网络爬虫技术收集数据，并结合TF-IDF算法、词云图和LDA主题模型等文本挖掘方法，深入分析电影消费者的情感倾向和兴趣偏好，挖掘评论中隐含的主题信息，发现观众对电影多数持积极态度并比较关注电影的剧情，演员、演技等属性特征。基于此，本文提出针对性的网络营销策略，优化电影行业的营销手段，让用户的评论文本数据发挥出最大的价值。In the current Internet age, movie reviews have emerged as a crucial platform for audiences to articulate their viewing experiences and emotions. Meanwhile, they also serve as a vast treasure trove of data, holding significant value for film creation and online marketing. As competition in the film market grows increasingly fierce, the question of how to effectively harness the emotional cues embedded in online reviews and develop precise online marketing strategies has garnered widespread attention within the film industry. This study focuses on the review texts of the top five movies from the Top 250 list on Douban website. By employing Python web-crawling technology to collect relevant data, and integrating multiple text-mining techniques including the TF-IDF algorithm, word-cloud visualization, and the LDA topic model, we delve into the emotional tendencies and interest preferences of film audiences. Furthermore, we explore the thematic information underlying the reviews. The findings reveal that audiences generally hold a positive attitude toward these movies and show a higher level of concern for certain attribute characteristics of the films, such as plot, actors, and acting skills. Based on these insights, this paper puts forward targeted network marketing strategies aimed at optimizing the marketing approaches in the film industry. The ultimate goal is to maximize the value derived from user-generated comment text data.