Personality Prediction of Social Network Users using LSTM based Sentiment Analysis
1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1861111
ABSTRACT
The rapid increase in posting personal information on social media has been noticed over last two years especially due to heavy use of smart phones and restrictions imposed on face-to-face meeting due to the current Covid-19 pandemic. Often, user information and user status updates on social media platforms provide valuable insights in determining personality traits. Scraping and analyzing this social media content and accurate prediction of user personality bring many benefits for enterprises including delivering personalized services, product recommendation and improving performance of recruitment system. This study was carried out based on digital footprints left on social media and the Myers-Briggs Type Indicator (MBTI), one of the most reliable personality inventories, was used to identify and predict the personality traits in mobile applications. Long Short-Term memory (LSTM) architecture-based sentiment classifier was proposed in this paper to predict personality and obtained a convincing model performance with overall 78% accuracy for personality traits. © 2022 IEEE.
LSTM; Machine Learning; Natural Language Processing; Personality prediction; Social Media Footprints; Forecasting; Learning algorithms; Sentiment analysis; Smartphones; Social networking (online); Face-to-face meetings; Machine-learning; Network users; Personal information; Personality predictions; Personality traits; Smart phones; Social media; Social medium footprint; Long short-term memory
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
1st IEEE International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2022
Year:
2022
Document Type:
Article
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