Sentiment analysis is a natural language processing (NLP) based computational approach, helps machines understand and categorize human emotions expressed in unstructured textual format. Traditional models, such as long short term memory (LSTM) networks, are good at recognizing patterns in text but often lack interpretability and struggle to include domain specific sentiment knowledge. These issues been addressed in this study, proposes a new hybrid model called attention based LSTM with VADER driven domain specific feature integration for sentiment analysis which uses an attention mechanism to focus on the most relevant words in the text and VADER sentiment scores as extra features related to specific domains, improving the model's ability to capture emotional sentiment. Sentiment analysis becomes more accurate and stable with the combination of both the LSTM's contextual understanding with VADER's sentiment knowledge. The experimental results claims, this proposed ensemble method improves on traditional LSTM models in terms of accuracy and adaptability, especially when handling specific domain specific sentiment analysis activities, to create more intelligent and context aware sentiment analysis model.