CONCLUSION After conducting a sentiment analysis of Twitter data using various machine learning algorithms, including Decision tree, Naive Bayes, KNN, CNN, and Random Forest, it was found that the CNN algorithm performed the best with the highest accuracy compared to the other models. CNNs are a type of deep learning algorithm that are particularly well-suited for image recognition, but they have also shown to be highly effective in natural language processing tasks such as sentiment analysis. The reason behind this is that CNNs can capture the complex relationships and patterns within text data through its layers of convolutions. Furthermore, it is worth noting that the other algorithms, such as Naive Bayes, Decision tree, KNN, and Random Forest, also performed well in sentiment analysis, but were outperformed by CNN. Each of these algorithms has its own strengths and weaknesses, and choosing the best algorithm for a particular task will depend on various factors, including the nature of the data and the problem at hand.
In conclusion, the sentiment analysis of Twitter data revealed that CNN is a powerful algorithm with highest accuracy of 75.88% in analyzing sentiment in text data. However, further research and testing are required to determine the optimal algorithm for specific tasks and datasets.