In the digital age, online reviews have become a crucial source of information influencing consumer decisions, especially in the entertainment industry. This study focuses on leveraging machine learning techniques to perform sentiment analysis on movie reviews, aiming to automatically classify them as positive, negative, or neutral. By utilizing natural language processing (NLP) techniques such as tokenization, stop-word removal, stemming, and vectorization (TF-IDF, Word2Vec), the raw textual data is transformed into structured formats suitable for machine learning algorithms. The results indicate that machine learning can effectively interpret user sentiment, enabling valuable applications such as recommendation systems, content moderation, and audience analysis. Furthermore, the study discusses the challenges in sentiment classification, such as sarcasm, context ambiguity, and imbalanced data. Future work may involve incorporating multimodal sentiment analysis by integrating text with audio and visual cues from movie trailers and reviews to enhance prediction accuracy.
Volume 17 | Issue 4
Pages: 34-39