The rapid growth of social media platforms has resulted in an overwhelming amount of user-generated data, which provides valuable insights into people's opinions, thoughts, and feelings. Sentiment analysis is the process of identifying and categorizing emotions expressed in text data. With the help of deep neural networks, sentiment analysis on Twitter data has become more sophisticated, allowing for a more comprehensive understanding of the sentiments expressed on this platform. This study aims to provide a summative analysis of sentiments on Twitter data using a deep neural network. The research utilizes a dataset of tweets collected over a period of six months from different regions and demographics. The study employs a deep neural network model that uses a combination of convolutional and recurrent neural networks to classify the tweets into three categories - positive, negative, and neutral. The results of the analysis show that the deep neural network model achieved an accuracy of 98%, indicating its effectiveness in classifying tweets into their respective sentiment categories. The study also revealed that positive sentiments were the most expressed emotions, followed by neutral and negative sentiments. Furthermore, the analysis identified the most used words associated with each sentiment category, providing insights into the language and context used to express emotions on Twitter.