## 1 Introduction
To address this gap, our study uses new metrics called retrieval-augmented generation assessment (RAGAS) combined with sentiment intensity, the level of detail in sentiment classification, and the Vendi score with auto-regressive language models (ARLMs) such as Llama-2 (available online: [[https://llama.meta.com/llama2/](https://llama.meta.com/llama2/)]([https://llama.meta.com/llama2/](https://llama.meta.com/llama2/)) (accessed on 18 September 2024)), Llama-3 (available online: [[https://llama.meta.com/](https://llama.meta.com/)]([https://llama.meta.com/](https://llama.meta.com/)) (accessed on 18 September 2024)), OpenAI GPT-3.5-Turbo (available online: [[https://platform.openai.com/docs/models/gpt-3-5-turbo](https://platform.openai.com/docs/models/gpt-3-5-turbo)]([https://platform.openai.com/docs/models/gpt-3-5-turbo](https://platform.openai.com/docs/models/gpt-3-5-turbo)) (accessed on 18 September 2024)) and OpenAI GPT-4o-Mini (available online: [[https://platform.openai.com/docs/models/gpt-4o-mini](https://platform.openai.com/docs/models/gpt-4o-mini)]([https://platform.openai.com/docs/models/gpt-4o-mini](https://platform.openai.com/docs/models/gpt-4o-mini))(accessed on 18 September 2024)) that have the capability to predict the next sequence of words in a sentence or text, and they have significantly expedited recent developments in NLP tasks like sentiment analysis. In this paper, we present a language feature extraction and adaptation for reviews (LFEAR) based on ARLMs, which we combine with retrieval-augmented generation (RAG) and fine-tuning in conversation structure. RAG aids in better understanding natural language in text, while the conversation structure uses ARLMs to capture word dependencies to improve the overall performance of NLP such as sentiment analysis. In response to challenges in sentiment analysis of financial product reviews, LFEAR is designed to be adaptable and high-performing across various domains, extending its effectiveness beyond its original target. LFEAR employs techniques such as fine-tuning in conversation format, efficient prompts, and continuous learning, then trains on products and reviews based on South African financial institutions to achieve 92.76% accurate outcomes on the long form of sentiment on financial reviews on Hollpeter. These enhancements underscore the critical necessity for sentiment analysis models that are detailed yet adaptable beyond their original training parameters. The primary contributions of this study can be summarised as follows:  
* We introduce LFEAR, a brand-new advanced inference model that uses conversational fine-tuning to auto-regressive LLMs like Llama-2, Llama-3, GPT-3.5-Turbo, and GPT-4o Mini to fit the complex analysis of South African financial product reviews. * LFEAR amalgamates, with prompt engineering, RAG and continuous learning to handle sarcasm features and calculate sentiment in other domains, such as financial news, political discourse, restaurant reviews, hotel feedback, and movie ratings. * We use the majority voting method to evaluate LFEAR on a wide range of different datasets and measure the relationship between public opinion and institutional performance. This demonstrates that the LFEAR is adaptable and strategic relevance across various contexts. * LFEAR improves sentiment analysis performance evaluations by incorporating RA-GAS, sentiment intensity, granularity classification, and vendi score, enhancing customer responses and bridging the gap in current financial sentiment analysis metrics. The remainder of this paper is organised as follows. Section 2 provides a brief review of the background and related work, highlighting previous studies on sentiment analysis in the financial sector. Section 3 details our methodology and describes the proposed framework for data collection, preprocessing, and model training. In Section 4, we present the performance evaluation of the models and offer a comparative analysis based on various metrics.