Sentiment analysis, a pivotal field in natural language processing, has evolved to encompass the intricate interplay of emotions within textual data. This paper introduces a pioneering approach to sentiment analysis by amalgamating deep learning techniques and geographic context within Twitter data and leveraging an expanded sentiment class set that includes positive, negative, neutral, mixed, ambiguous, happy, sad, angry, fearful, and surprised. Our framework aptly captures diverse emotional expressions. Incorporating location-based sentiment analysis unveils cross-border sentiment dynamics, enriching our understanding of how emotions resonate within various geographical regions. We present a meticulously designed deep learning model that integrates textual content and location information seamlessly. Our model achieves exceptional accuracy, precision, recall, and F1-score values by employing text vectorization, embedding layers, and advanced classification techniques. The temporal analysis of tweet timestamps uncovers temporal engagement trends, while the examination of tweet lengths underscores the dynamic range of expression within the Twitter character limitation. Furthermore, our investigation into the locations reveals Twitter's global presence, with the United States, United Kingdom, and Ukraine emerging as key hubs of activity. This geographical insight augments our comprehension of the platform's diverse user interactions. This paper offers insights into sentiment analysis and paves the way for future research exploring sentiment dynamics, language variations, and real-time interactions within the Twitter landscape.