## 4 Experiments
### Evaluation of Inference Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Domain** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Proposed Inference Model & Hellopeter & Financial Products & 0.9276 & 0.8765 & 0.8806 & 0.9123 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Proposed inference model performance evaluation on the Hellopeter dataset. To comprehend the robustness of the model when it comes to differing emotional intensities present within the data, we also assessed it against several sentiment intensity metrics. In Table 10, we display the breakdown of these metrics for LFEAR. What is presented is a distribution of not only the sentiment polarity that is associated with our LFEAR outputs but also the average Verdi scores across several categories of sentiment intensity, including categories like Moderately Negative, Moderately Positive, Strongly Negative, and Strongly Positive. When it comes to results from the model for negative sentiments, we obtain a robust average polarity score of about -0.806 and even more so for our associated Verdi score, which averages about -0.854, indicating that we have a pretty high confidence level in our detection of instances of intense negativity in our dataset. On the other end of the spectrum, when we test some strongly positive sentiment, we obtain virtually the same average sentiment polarity score of 0.806, which suggests that our positive sentiment detection capabilities are almost as effective as our negative ones. These outcomes are illustrated in greater detail in Figure 10. The plot in Figure 11 shows the performance of RAGAS. The performance metrics shown here suggest that RAGAS is not only proficient in understanding kinds of queries that are specific to our context but that it is also excellent in terms of answer quality and precision. Figure 12 visualises the distribution of sentiment polarity and Verdi scores across the Hellopeter dataset, highlighting the model's nuanced performance in handling different sentiment intensities. Figure 10: Confusion matrix for the proposed inference model on the Hellopeter dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dataset** & **Domain** & **RAGAS Metric** & **Average Score** \\ \hline \multirow{4}{*}{LFEAR} & & & Answer Similarity & 0.9845 \\  & & Answer Correctness & 0.9385 \\ \cline{1-1}  & & Answer Relevancy & 0.8775 \\ \cline{1-1}  & & Context Recall & 0.7897 \\ \cline{1-1}  & & Context Precision & 0.9769 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: RAGAS metrics for the proposed inference model. Hellopeter dataset, with both high precision (87.65%) and high recall (88.06%) comparable to F1 (91.23%). LFEAR's impressive performance on Hellopeter and other domains can be explained by the use of customised LLMs, RAG, and continuous learning techniques. The fine-tuning that is performed here on the local and business-related language helps to improve the accuracy of the model toward classifying complex sentiment patterns in the financial domain. The integration of RAG, which dynamically retrieves relevant context from an updated vector database, ensures that sentiment predictions remain contextually aligned with real-time market trends. Such a feature would be particularly important for financial companies that want to adjust their approach alongside the changes in consumers' attitudes. The performance metrics of the LFEAR model, as revealed in Tables 9 and 10, underscore its sturdiness and effectiveness in sentiment analysis, with a particular focus on financial product reviews. The RAGAS metrics are displayed in Table 9, and at their core, they provide a look at how well the LFEAR model answers questions posed to it. The key takeaway from the RAGAS evaluation is that the LFEAR model's answer accuracy is exceedingly high and very close to what one might expect from a human responder. The Answer Correctness metric of 0.9385 shows a very high level of accuracy.