• Anupriya
• Dr. Archana Singh
• Mr. Jitendra Singh

Classification of opinions using micro-blogging sites and tweets is a broad area of research. It may yield intriguing results and provide insights into social behaviour and public opinion regarding various services, products or events, geopolitical concerns and other scenarios and events that impact the world. In this paper we propose a solution that aims at multidimensional emotion classification that is based on the microblog's emotional classifying Twitter data by using the convolution neural network. In this paper, we will employ n-gram-based features on words using the word sentiment-polarity score feature to create tweets with sentiment features. This will result in a massive amount of data that could be obtained by unsupervised learning. It is intended to be used as a testing set and also a testing set that has cross-validation. Additionally, we will use these features to categorize emotion into five categories: joy, anger, sadness, fear, and surprise. We will also identify data by geotagged information. This feature collection is embedded deep into neural networks using convolution, and its performance is evaluated against other methods like SVM as well as Naive Bayes.

CNN, Twitter, Neural Network, Sentiment Analysis, SVM, Naive Bayes

"Sentiment Analysis of Twitter Data using DL Algorithm and CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 12, page no.c633-c638, December-2022, Available :http://www.jetir.org/papers/JETIR2212288.pdf

"Sentiment Analysis of Twitter Data using DL Algorithm and CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 12, page no. ppc633-c638, December-2022, Available at : http://www.jetir.org/papers/JETIR2212288.pdf