TY - GEN
T1 - Social Media Sentiment Analysis Using Deep Learning Approach
AU - Iqbal, M. Mohamed
AU - Arikumar, K. S.
AU - Venkateswaralu, Balaji Vijayan
AU - Ahamed, S. Aarif
N1 - Publisher Copyright:
© 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Sentiment analysis
KW - deep learning
KW - social network
UR - http://www.scopus.com/inward/record.url?scp=85171324415&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-35078-8_36
DO - 10.1007/978-3-031-35078-8_36
M3 - Conference contribution
AN - SCOPUS:85171324415
SN - 9783031350771
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 431
EP - 438
BT - Intelligent Systems and Machine Learning - 1st EAI International Conference, ICISML 2022, Proceedings
A2 - Nandan Mohanty, Sachi
A2 - Garcia Diaz, Vicente
A2 - Satish Kumar, G.A.E.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022
Y2 - 16 December 2022 through 17 December 2022
ER -