The system includes sentiment-aware recommendation capabilities, enabling it to tailor suggestions based on users' emotional responses. It also tracks general sentiment across social media platforms to understand public opinion. Using advanced language representation models, it supports a wide range of sentiment analysis tasks. Additionally, the system is designed to adapt to evolving ways in which sentiment is expressed over time.

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The FT-TSA model is carefully built to capture sentiment nuances and adapt to changing language and environment.

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It achieves state-of-the-art sentiment classification using comprehensive data preparation, tokenization, and parameter tuning. FT-TSA outperforms traditional sentiment analysis models and generic Transformers in complex or context-dependent attitudes, according to empirical assessments on standard sentiment analysis datasets. We consider the implementation to pre-train a smaller general-purpose language representation model, DistilBERT, which can be fine-tuned to perform well on a wide range of tasks like its larger equivalents. This research shows FT-TSA's adaptability by applying it to sentiment-aware recommendation systems and social media sentiment tracking.

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This research introduces the Fine-Tuned Transformer for Sentiment Analysis (FT-TSA), an advanced AI model designed to better understand and analyse complex emotions in text. It uses improved data preparation, tokenisation, and tuning to achieve high accuracy in identifying sentiment, even in context-heavy or subtle expressions. Built on the lightweight DistilBERT model, FT-TSA is efficient yet powerful, outperforming traditional sentiment analysis tools and general Transformer models. It can be applied to real-world tasks such as personalised recommendations and tracking public sentiment on social media, making it a flexible and practical solution for modern language understanding.

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