• Bhavesh Bhadane
• Abhishek Chopda
• Siddhant Bhope
• Hardik Patel
• Prof. Monali Bansode

In this paper, we have presented an experiment that can easily detect categories of emotions like Happiness , surprise, neutral, sad, fear and hate on Twitter platform. Twitter is micro blogging social media site where many users shares their opinions and discuss about various issues. Many events occurring around the world can be known from this platform, so we can detect the emotions and find out various issue like fear, depression and hate among society and can easily get solution to prevent it from happening. we have used tweepy, a twitter based API that can get tweets easily from users and hashtags and then we pre-processed those tweets with modern methods of NLPs. After that, To classify those tweets into different emotions, we have created CNN-LSTM model that is giving 89.4% accuracy i.e better than traditional machine learning models.

NLP, Tweepy, LSTM, RNN, TF-IDF.

"Automated Emotion Analysis on Twitter Using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c72-c80, May-2022, Available :http://www.jetir.org/papers/JETIR2205312.pdf

"Automated Emotion Analysis on Twitter Using Machine Learning and Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc72-c80, May-2022, Available at : http://www.jetir.org/papers/JETIR2205312.pdf