Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.

In an increasingly complex and dynamic business environment, employee job performance remains a critical determinant of organizational success. Emotional intelligence (EI) and leadership styles are widely acknowledged as key factors shaping workplace behavior, employee engagement, and overall productivity. EI, encompassing competencies such as self-awareness, emotional regulation, and social skills, enables leaders to cultivate trust, manage team dynamics, and enhance decision-making processes. Leadership styles, including transformational, transactional, and laissez-faire approaches, serve as strategic frameworks through which leaders influence employee performance and organizational outcomes. While extensive research has examined EI and leadership styles independently, there remains a paucity of studies investigating their combined influence and the underlying mechanisms that moderate this relationship across diverse organizational and cultural contexts. This review synthesizes existing literature to critically evaluate the intersection of EI and leadership styles, examining their collective impact on employee performance and identifying key moderating variables. Through a systematic analysis of recent empirical studies, the review highlights emerging trends, theoretical implications, and practical applications in leadership effectiveness. Additionally, it identifies crucial areas for future research, including cross-cultural variations, AI-integrated leadership paradigms, and the longitudinal effects of EI on employee outcomes. By addressing these gaps, this study contributes to the evolving discourse on leadership and organizational behavior, offering insights for both scholars and practitioners.