摘要
在数字化时代背景下，随着互联网技术的迅速发展，大量的数据在网络上被产生和共享，其中包括对文化产品如电影的公众评价。这些评价数据不仅蕴含着丰富的信息，反映了公众的情感态度和偏好，也为文化产品的制作、推广以及评估提供了宝贵的数据资源。基于此，本研究致力于利用Python语言和其强大的库资源，开发了一套系统性的方法，对豆瓣电影的评论数据进行自动化采集、处理、分析以及可视化展示，旨在深入探索和分析公众对电影的评价与感受。
研究首先采用Selenium工具实现对豆瓣电影评论的自动化采集，通过模拟真实用户的浏览行为，高效获取数据。随后，利用Pandas进行数据的清洗和预处理，确保数据质量。在文本分析阶段，结合Jieba分词和NLPIR等自然语言处理工具，对评论文本进行了精确的分词、词性标注，并通过TF-IDF等算法提取关键词，揭示评论中的热点内容和情感倾向。此外，本研究还应用了情感分析技术，评估了评论的情绪色彩，区分了正面、中性和负面评论，为电影的情感评价提供了量化的指标。在数据可视化方面，研究利用Matplotlib和Seaborn等工具，将复杂的分析结果以直观的图表和图形形式展现，使得分析发现更易于理解和传达。
通过上述研究，本文不仅展示了Python及其相关库在处理和分析大规模文本数据方面的强大能力，也为电影制作人、分析师和文化研究者提供了直观的洞察，帮助他们更好地理解市场和观众反馈。展望未来，本研究的方法和流程有望应用于更广泛的领域，如书籍、音乐等其他文化产品的评价分析，进一步推广数据科学在人文社会科学研究中的应用，为理解现代文化消费模式和公众情感倾向提供新的视角和方法。此外，研究还将探索更多维度的数据分析和采用更先进的机器学习模型，以提高分析的准确性和深度，为文化产业的发展提供更为精准的数据支持。
关键词：自动化数据采集、情感分析、数据可视化
ABSTRACT
In the context of the digital age, with the rapid development of Internet technology, a large amount of data is generated and shared on the Internet, including the public evaluation of cultural products such as movies. These evaluation data not only contain rich information and reflect the emotional attitudes and preferences of the public, but also provide valuable data resources for the production, promotion and evaluation of cultural products. Based on this, this study is committed to using the Python language and its powerful library resources to develop a systematic method to automatically collect, process, analyze and visualize the review data of Douban movies, aiming to deeply explore and analyze the public's evaluation and feelings about movies.
Firstly, the Selenium tool was used to realize the automatic collection of Douban movie reviews, and the data was efficiently obtained by simulating the browsing behavior of real users. Subsequently, Pandas is used for data cleaning and preprocessing to ensure data quality. In the text analysis stage, combined with natural language processing tools such as Jieba word segmentation and NLPIR, the comment text was accurately tokenized and tagged by part of speech, and keywords were extracted through algorithms such as TF-IDF to reveal the hot content and emotional tendency in the comment. In addition, this study also applied sentiment analysis techniques to evaluate the emotional color of reviews, distinguish positive, neutral and negative reviews, and provide quantitative indicators for the emotional evaluation of films. In terms of data visualization, the study uses tools such as Matplotlib and Seaborn to present complex analysis results in intuitive charts and graphs, making the analysis findings easier to understand and communicate.
Through the above research, this paper not only demonstrates the powerful capabilities of Python and its related libraries in processing and analyzing large-scale text data, but also provides intuitive insights for filmmakers, analysts, and cultural researchers to better understand the market and audience feedback. Looking forward to the future, the methods and processes of this study are expected to be applied to a wider range of fields, such as the evaluation and analysis of books, music and other cultural products, to further promote the application of data science in humanities and social science research, and to provide new perspectives and methods for understanding modern cultural consumption patterns and public emotional tendencies. In addition, the research will also explore more dimensions of data analysis and adopt more advanced machine learning models to improve the accuracy and depth of analysis, and provide more accurate data support for the development of the cultural industry.
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