Expanding Movie Sentiment Analysis with Fastai
DOI:
https://doi.org/10.62051/gzc1np77Keywords:
Sentiment Analysis; Fastai; Natural Language Processing; Deep Learning; Text Classification.Abstract
Sentiment analysis, a crucial component of natural language processing (NLP), isolates personal information from intellectual information, such as digital responses. Moving learning-based deep learning systems like Fastai have performed incredibly well in general text classification. However, they haven't been tested on their adaptability to domain-specific tasks like video sentiment analysis. Applying the Kaggle videos, this research examines Fastai's success. Using worksheet data to make sentiment labels implementing textual inputs and user ratings. The paper assesses the capabilities and limitations of Fastai's AWD- Classification structures through broad data preprocessing, type great-tuning, and several multifaceted testing situations. Experimental results reveal an accuracy of 94 % on standardized tests; however, they expose threats in addressing extended or quiet functions. The paper proposes employing impressive structures and data augmentation strategies to address these issues. The paper's results show Fastai's skills and areas for improvement and provide helpful recommendations for its effectiveness in practical programs.
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