## 4 Experiments
### Evaluation of Inference Model
The Context Precision of 0.9769 indicates an extraordinary ability to put the answers in the proper context. These admirable levels of performance make the model a prime candidate for real applications where relevant answers and accurate context are not just desirable to have but necessary, especially in applications like finance that require pinpoint sentiment differentiation. The data in Table 10 reinforce the impression that LFEAR handles not just sentiments but also sentiment intensity quite well. In the Hellopeter dataset, the average sentiment polarity for Strongly Negative is \(-0.806115\), and for Strongly Positive, it is 0.806144. These numbers show that LFEAR is not only recognising the emotional expression of this data but also scoring it with excellent precision. The model has an average Vendi score of \(-0.853706\) for Strongly Negative sentiments, which shows it has a really good ability to detect high-intensity dissatisfaction. On a practical level, that is a really good feature to have when modeling customer sentiments; it indicates when your customers are showing service dissatisfaction. The quantitative outcomes undoubtedly underscore LFEAR's state-of-the-art performance. They exhibit the model's ability to address the two fundamental research gaps in sentiment analysis--that is, precision and context awareness. Both sentiment misclassification and inadaptability to the real-world context often plague traditional models. By leveraging fine-tuning and RAG integration, LFEAR can not only detect accurate sentiment but also achieve real-time contextual adaptation, leading traditional sentiment analysis models into a new era of performance. This is evidenced in Tables 9 and 10, where the LFEAR model is shown to exhibit consistent appearance and performance across a wide variety of sentiment types and domains--something traditional models have been unable to achieve. To further this model's real-world adaptability, we have incorporated a capacity for continuous learning combined with scalable inference layers. LFEAR's high RAGAS scores, sentiment polarity accuracy, and ability to generalise across domains affirm its scalability, robustness, and suitability as a leading sentiment analysis tool in industry-specific applications. The capacity of LFEAR to handle the different intensities of sentiment is highlighted in Figure 13a,b. These show how well LFEAR distinguishes not just between positive and negative sentiments but also between sentiments of varying strength--a distinction that is crucial for many applications. Indeed, LFEAR appears to achieve state-of-the-art performance in this respect. Figure 14a,b illustrate and provide some further detail regarding this aspect of LFEAR's performance. The evaluation of the model across domains leaves no doubt with regard to their robustness and flexibility. While mostly trained on financial review datasets, the model has been shown to work well on other datasets, namely restaurant, movie, hotel, or even political reviews. What is responsible for this kind of flexibility is the fact that the model utilises a cross-domain architecture comprising fine-tuned layers for specific domains alongside a scalable inference layer that is capable of processing diverse sentiment contexts. Additionally, continuous learning capabilities enhance the model's ability to learn over time and reinforce its ability to meet intelligence patterns, which change with the attitudes of people as the system mostly learns from customers. However, the constant efficacy illustrated over the different domains leads us to the conclusion of the validity of the proposed methodology and broadens the areas of its utilisation beyond finance to healthcare, retailing, and public opinion monitoring. The model's scalable and removable structure allows embedding it into practical applications without any performance loss. Its features of continuous learning and the ability to continually update the vector database make it robust to increased volumes of data and changing contexts.