Year: 2025
Type: article
Abstract: 随着互联网技术的发展，电影评论网站积累了大量用户评论数据。对这些评论进行情感分析不仅能够帮助用户快速了解电影口碑，也为电影产业提供有价值的决策参考。本研究以电影《盗梦空间》(Inception)的英文评论数据为研究对象，采用TextBlob工具对评论数据进行情感分类，分析评论的极性分数以判断观众对该电影的情感倾向为积极或消极，同时引入VADER工具进行补充，进一步分析影评中的情绪强度分布，以增强情感分析的深度和多维度表现。研究结果表明，绝大多数评论呈现出积极情感倾向，显示《盗梦空间》在观众中具有较高的认可度。然而，也有一部分评论表达了消极情绪，主要集中在对情节复杂性和结局开放性的不满。本研究验证了TextBlob在情感分析任务中的有效性，并展示了其在电影评论情感分析中的应用价值，为电影评论情感分析提供了有效的实践方法。With the rapid development of internet technologies, movie review platforms have accumulated a large volume of user-generated content. Analyzing the sentiment of these reviews not only helps users quickly grasp the general reception of a film but also provides valuable insights for decision-making in the film industry. This study focuses on English-language reviews of the movie Inception and employs the TextBlob to perform sentiment classification. By analyzing the polarity scores of the reviews, the study determines whether viewers’ attitudes toward the movie are generally positive or negative. At the same time, the VADER was introduced as a supplement to further analyze the distribution of sentiment intensity in the reviews, enhancing the depth and multidimensionality of the sentiment analysis. The findings indicate that the vast majority of reviews exhibit a positive sentiment, reflecting a high level of audience approval for Inception. However, a portion of the reviews expresses negative sentiments, primarily related to dissatisfaction with the film’s complex plot and open-ended conclusion. This research confirms the effectiveness of TextBlob in sentiment analysis tasks and demonstrates its practical value in analyzing movie reviews, offering an efficient method for sentiment assessment in this domain.
Source: Modern Linguistics
Author: 雨婷 王
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412422303