Scientists show validity of using socialmedia to predict users' psychologicalwell-being
]. Therefore, in this study, we chose the spectrum of individualism-collectivism, which is an important expressive dimension of cultural value, to extract features for the purpose of predicting PWB.First, we randomly sent invitations to approximately 20,000 Weibo users.
During the first step of model training, all features were normalized to ensure that the feature contributions to the model were not affected by their range and distribution. Given that the tree model has a built-in feature selection function, no feature selection nor feature reduction was performed to retain as much information as possible. Although some categories may have had some of the same words, this would not have impacted the prediction because it did not add new information [].
After feature normalization, we got a sample size of 1427 with 120 features for each sample. We then used multiobjective extra trees to develop a regression prediction model. As an extension of the random forest regression model [], the extra trees algorithm was proposed as a computationally efficient and highly randomized extension of the random forest algorithm.
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