Open AccessSocial Sentiment Analysis Using Classifiers and Ensemble Learning
Author(s) -
Linxiang Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/2/022193
Subject(s) - computer science , sentiment analysis , aggregate (composite) , ensemble learning , graph , artificial intelligence , machine learning , social graph , social media , data science , information retrieval , world wide web , theoretical computer science , materials science , composite material
Instant message applications has become the trend of present social network for connecting people nowadays. With such massive information transmitted between end users, it may provide valuable hidden knowledge for data analysing and help us to obtain some insights of how the messages can influence current social trends. During the analyses, the author uses several different classifiers to conduct sentiment collection and use Ensemble Learning to aggregate the results. As a result, the author maps data about users’ sentiment graph in database of given category.