
Twitter Sentiment Analysis Using Deep Learning
Author(s) -
Neha,
H. K. Gupta,
Sagar Pande,
Aditya Khamparia,
Vaishali Bhagat,
Nikhil E. Karale
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1022/1/012114
Subject(s) - sentiment analysis , computer science , deep learning , artificial intelligence , product (mathematics) , data science , natural language processing , machine learning , information retrieval , mathematics , geometry
It is well established that the tweet sentiment analysis is still focused on conventional messages, such as film reviews and product reviews, while significant improvement has been made as deep learning becomes widespread, and comprehensive data sets are accessible for training (far from just emoticons and hashtags). Nevertheless, prior opinion analysis experiments typically performed on tweets, i.e. only two forms of global polarities (i.e. optimistic and negative) occur with their work/validation/test data sets. What is more, systems’ judgments are not actively aligned with the specified appraisal objects. In this paper, we have discussed some deep learning approaches for twitter sentiment analysis. We also trained our model using CNN and RNN to get some good accuracy results.