TWITTER SENTIMENT ANALYSIS **This files includes visualizations,recognizing the tweets data emotions(i.e Positive , Negative ) firstly,importing necessary libraries(pandas, numpy, re, seaborn, matplotlib, and nltk). and for evaluating we used the dataset(train_tweet, test_tweet). We implemented polarity function to check whether the tweet is positive , negative , Neutral.. and we used Logistic regression,SVM algorithms for finding accuracy and we used performance metrics that help measure the reliability of a classification model. We did Preprocessing operations, including stopword handling using nltk. and handled the data by combining dataset using pd.concat. Run the notebook to see its outputs and check for errors. Review specific sections for functionality or optimization.