Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect Conditioning Layer (AACL) that injects aspect information prior to transformer-based contextual encoding. The proposed framework follows a structured pipeline comprising tokenization, token and positional embeddings, AACL, transformer encoding, BiLSTM refinement, aspect-guided attention, fully connected projection, and focal loss optimization. By conditioning token representations before contextual encoding, the framework enables aspect-aware contextual learning that better captures sentiment-relevant semantic dependencies. Subsequent sequential refinement and attention-based reasoning further enhance sentiment polarity alignment while improving model interpretability. To evaluate the proposed approach, a robust ABSA dataset containing approximately 10,000 recent reviews annotated across five sentiment intensity levels was developed. Extensive experiments demonstrate that HABSA and AAC-HABSA consistently outperform transformer-only and conventional hybrid baselines in terms of accuracy, macro-F1 score, robustness, and attention-based interpretability. The proposed framework provides a computationally efficient, mathematically coherent, and interpretable solution for fine-grained sentiment analysis. By strengthening aspect-aware representation learning within transformer and LLM-oriented sentiment analysis pipelines, this work contributes to the development of scalable and deployable AI-driven opinion analytics systems across real-world domains.

With growing urban areas, the climate continues to change as a result of growing populations, and hence, the demand for better emergency response systems has become more important than ever. Human Behaviour Classification (HBC) systems have started to play a vital role by analysing data from different sources to detect signs of emergencies. These systems are being used in many critical areas like healthcare, public safety, and disaster management to improve response time and to prepare ahead of time. But detecting human behaviour in such stressful conditions is not simple; it often comes with noisy data, missing information, and the need to react in real time. This review takes a deeper look at HBC research published between 2020 and 2025 and aims to answer five specific research questions. These questions cover the types of emergencies discussed in the literature, the datasets and sensors used, the effectiveness of machine learning (ML) and deep learning (DL) models, and the limitations that still exist in this field. We explored 120 papers that used different types of datasets, some were based on sensor data, others on social media, and a few used hybrid approaches. Commonly used models included CNNs, LSTMs, and reinforcement learning methods to identify behaviours. Though a lot of progress has been made, the review found ongoing issues in combining sensors properly, reacting fast enough, and using more diverse datasets. Overall, from the findings we observed, the focus should be on building systems that use multiple sensors together, gather real-time data on a large scale, and produce results that are easier to interpret. Proper attention to privacy and ethical concerns needs to be addressed as well.