To build this project i have used the Anaconda Software (https://www.anaconda.com/download/) and For this project, i have used a CSV file which contains some random tweets and by using the machine learning algorithms like Logistic Regression, Bernoulli Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest we are calculating the F1 score and Accuracy score and in our project, the Random Forest algorithm is giving the highest F1 score and Accuracy Scores and it describes that the Random Forest algorithm is best suited for our project to determine a perticular tweet is positive or negetive.
open the Twitter Sentiment Analysis.ipynb file in jupyter IDE and run each and every block separately.