From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification
arXiv:2605.22003v1 Announce Type: cross Abstract: Sentiment analysis, also referred to as opinion mining, primarily tries to extract opinion from any text-based data. In the context of movie reviews and critics, sentimental analysis can be a helpful tool to predict whether a movie review is generally positive or negative. It can be difficult for the ML models to understand the context or metaphysical sentiment accurately, as ML models rely largely on statistical word representations. The objective of this paper is to examine and categorise movie reviews into positive and negative sentiments. D
The continuous evolution of AI models, particularly transformers, is enabling more nuanced and effective natural language processing, making sentiment analysis a rapidly advancing field.
Improved sentiment analysis techniques can enhance understanding of public opinion, consumer behavior, and media reaction, providing valuable insights for various industries and strategic decision-making.
The application of advanced AI models like transformers allows for more accurate and context-aware sentiment classification, moving beyond simpler statistical methods.
- · AI/ML researchers
- · Social media analytics platforms
- · Marketing and advertising firms
- · Customer service solutions
- · Legacy sentiment analysis algorithms
- · Companies relying on basic text processing
More accurate sentiment analysis tools become available for commercial use.
Businesses gain deeper insights into customer feedback and market trends, leading to more data-driven strategies.
The development of more sophisticated AI agents capable of understanding and responding to complex human emotions could accelerate.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG