<![CDATA[The adaptation of video games into films or television series has increasingly become a prominent trend in the entertainment sector, often eliciting diverse reactions from audiences.A prime example is The Last of Us, a video game adaptation series that generated substantial online discussions and sentiment, and serves as the specific case study in this research. Sentiment patterns found in audience reviews of The Last of Us on IMDb are analyzed using a domain-specific classification framework tailored to the language characteristics of entertainment media. A key issue addressed is the discrepancy between numerical ratings and the sentiment conveyed in review texts, which may lead to inconsistent labeling. The study employs a machine learning technique, Support Vector Machine (SVM), coupled with two distinct labeling methods: manual labeling based on IMDb ratings, and automatic labeling using the lexicon-driven VADER tool. A total of 2,017 English reviews of The Last of Us were gathered via web scraping from IMDb, followed by preprocessing, TF-IDF feature extraction, and hyperparameter optimization using RandomizedSearchCV. These results show that the SVM model trained on VADER-labeled data achieved an accuracy of 0.97, outperforming the model trained on manually labeled data at 0.79. Lexicon-based automatic labeling provides more consistent and reliable sentiment classification, particularly in specialized domains like video game adaptation reviews. Integrating VADER labeling with SVM enhances sentiment analysis effectiveness and offers practical value for media analytics, content creation, and audience insight research.]]>
