## 3 Proposed Method
### Preprocessing Layer
#### 3.2.3 Prompt Engineering  
Prompt engineering is one of the important aspects in driving the model to the relevant sentiment classification, particularly in domains such as financial product reviews. The design of prompts uses state-of-the-art methods like few-shot learning, chain-of-thought reasoning, and ReAct (reason and act). These strategies are employed in a very strategic way to increase the model's capacity to understand sentiment in detailed reviews. The detailed process of our prompt engineering strategy is outlined in Algorithm 2. ```
0: Input text \(T\) (customer review in natural language)
0: Pre-defined prompts for Few-shot Learning, Chain-of-Thought Reasoning, and ReAct techniques
0: Sentiment classification output \(S\in\{\)positive, negative\(\}\)
1: Step 1: Apply Few-shot Learning
2: Construct few-shot learning prompt \(P_{\textit{fewshot}}\), with labeled example reviews
3: Generate initial classification prediction \(S_{\textit{fewshot}}\) using \(P_{\textit{fewshot}}\)
4:if\(S_{\textit{fewshot}}\) indicates ambiguity or mixed sentiment then
5: Proceed to Chain-of-Thought Reasoning for further clarification
6:else
7: return\(S_{\textit{fewshot}}\) as final sentiment classification
8: Step 2: Apply Chain-of-Thought Reasoning
9: Construct chain-of-thought prompt \(P_{\textit{CoT}}\) with guiding criteria (e.g., customer service, product features, satisfaction level)
10: Generate refined classification prediction \(S_{\textit{CoT}}\) based on \(P_{\textit{CoT}}\)
11:if\(S_{\textit{CoT}}\) is inconclusive or requires real-time adaptation then
12: Proceed to ReAct prompt for adaptive reasoning
13:else
14: return\(S_{\textit{CoT}}\) as final sentiment classification
15: Step 3: Apply ReAct (Reason and Act)
16: Construct ReAct prompt \(P_{\textit{ReAct}}\), focusing on aspects such as reasoning and decision-making
17: Generate final adaptive sentiment prediction \(S_{\textit{ReAct}}\) based on \(P_{\textit{ReAct}}\) and review of \(T\)
18:if\(S_{\textit{ReAct}}\) is confirmed with high confidence then
19: return\(S_{\textit{ReAct}}\) as final sentiment classification
20:else
21: Flag \(T\) for manual review if sentiment classification remains unclear
```  
**Algorithm 2** LFEAR Prompt Engineering Techniques  
**Few-shot Learning:** This technique is based on the fact that the model is trained on a subset of well-selected examples within the prompt and can generalise and classify new inputs correctly even with a small number of training samples [103], as shown in Figure 2. An example prompt used is as follows:  
Help me classify this customer review into positive or negative sentiment. Here are some examples:  
* \"I love the mobile app; it's very user-friendly\". (positive)
* \"Their customer support is unresponsive and unhelpful\". (negative)  
**Chain-of-Thought Reasoning:** This enhances the model's interpretability through a logical flow when classifying sentiment [104], as shown in Figure 3. This allows the model to consider elements such as service delivery, product attributes, and satisfaction levels before arriving at a classification. The prompt used is as follows:  
Below is a customer financial product review. Determine if the sentiment is positive or negative. Consider the following aspects:  
1. [label=]
2. **Review:** \"I have tried several times to get a IT3b statement for the 201718 tax year I have emailed several times and have not had a response I have registered for online banking but it says that I do not have transactional functionality so I cannot download the statement Can someone please help me\"
3.