Research Article
Social Media Sentiment Analysis Using Deep Learning Approach
@INPROCEEDINGS{10.1007/978-3-031-35078-8_36, author={M. Mohamed Iqbal and K. S. Arikumar and Balaji Vijayan Venkateswaralu and S. Aarif Ahamed}, title={Social Media Sentiment Analysis Using Deep Learning Approach}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Sentiment analysis deep learning social network}, doi={10.1007/978-3-031-35078-8_36} }- M. Mohamed Iqbal
K. S. Arikumar
Balaji Vijayan Venkateswaralu
S. Aarif Ahamed
Year: 2023
Social Media Sentiment Analysis Using Deep Learning Approach
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_36
Abstract
Compared to more traditional social media channels, Facebook and Twitter are far more effective at spreading information. Social media has developed into a great data origin for businesses or researchers to create models to analyse this repository and harvest practical insights for marketing policy for word-of-mouth (WOM) trading. However, the vocabulary used in social media is rather condensed and includes specialised words and symbols. Such brief communications are not well suited for the majority of natural language processing (NLP) techniques, which concentrate on processing formal phrases. In this paper, we suggest a brand-new paradigm for social media sentiment analysis based on deep learning models. We gather information from which we create a dataset. We aim to create a semantic dataset after processing these particular phrases in order to support future study. Future applications will benefit greatly from the retrieved data. Several social media platforms have been crawled to gather the trial data.
