## 7 Conclusions and Future Work  
With the proliferation of smart mobile devices, user reviews from app stores have become an essential data source for requirements engineers and researchers. ABSA has been proven to be effective for requirements elicitation and evolution from app reviews. However, existing FT and PT approaches often struggle with low-resource scenarios and noisy data conditions, limiting the generalization and robustness of PLMs. To address these issues, we proposed an ABSA model called LePB-SA4 RE, which integrates the PT method with a lexicon-enhanced dynamic modulation layer. The experimental results demonstrated that LePB-SA4 RE significantly outperforms SOTA models across different app review domains. The designed prompt template effectively enhances the ability of contextual information analysis and task-specific requirements elicitation. LePB-SA4 RE maintains robust performance even as training data decreases and exhibits significant robustness and generalization under noisy conditions. Additionally, the empirical study explores the potential mechanisms of various historical BERT checkpoints for requirements elicitation from app reviews, providing valuable insights for future research. Overall, this study provides a new perspective for evolving requirements elicitation. LePB-SA4 RE could be served as a valuable tool for enterprises seeking to optimize their software development processes. Our research team plans to focus on the following areas in future work:  
_Majorization Researches on Models with the Training Strategy of Whole-Word Masking (WWM)_: The results from RQ5 highlight the superior generalization capabilities of the WWM model. Future research will focus on integrating new neural network architectures into the existing framework to enhance the task-specific performance without sacrificing generalization. Potential directions include developing attention mechanisms tailored for whole-word context or exploring hybrid models that combine WWM with token-level processing. Additionally, as global markets continue to expand, the ability to conduct sentiment analysis across multiple languages is becoming critical. To facilitate efficient cross-lingual adaptation, we will investigate multilingual models such as BUC/BC-M or XLM-RoBERTa, leveraging zero-shot and few-shot learning techniques, to minimize the need for extensive additional training. _Optimizing State-of-the-Art Large Models for Practical Deployment_: Large language models such as GPT-4o, Claude 3.5,Llama 3, and Gemini 1.5 Pro have shown exceptional performance in various NLP tasks. However, their high computational requirements and deployment costs present significant challenges, especially for startups. Future research will explore methods to reduce model size without sacrificing performance. Techniques such as model distillation, pruning, quantization, and efficient FT approaches will be investigated to create lighter, more cost-effective versions of these models. Additionally, adaptive inference algorithms that dynamically adjust computational resources based on task complexity and real-time requirements will be explored. _Temporal Sentiment Drift and Dynamic Lexicon Updates_: A fundamental challenge in sentiment analysis is modeling temporal sentiment evolution across dynamic sociocultural landscapes. To address temporal sentiment drift, our future research will develop neural temporal modeling frameworks integrating temporal attention mechanisms with Transformer-based sequence decomposition techniques. These architectures will be specifically optimized for capturing multi-scale temporal dependencies in sentiment expression patterns, maintaining analytical relevance amid evolving user preferences through adaptive temporal alignment modules. Additionally, we plan to investigate an incremental lexicon evolution paradigm employing memory-augmented neural networks with online contrastive learning.