Short text classification with machine learning in the social sciences: The case of climate change on Twitter

The advantages of the LR classifier are its simplicity, efficient training, interpretability and the absence of assumptions about the class distribution. The downsides are overfitting for a large number of features (compared to the number of observations) and sensitivity to multicol- linearity among predictors. A classifier is characterized by a set of hyperparameters which refer to parameters related to the model’s architecture and whose values are set before the learning process begins. This is in contrast to the model’s own parameters which are learned during the training. The SciKit- Learn implementation of the LR classifier has several hyperparameters, including regulariza- tion strength and maximum number of iterations. For our numerical performance assessment, these are tuned to get the best classification performance. Support vector machines. Support vector machines (SVMs) were first proposed in [32] for linearly separable problems and later generalized to non-linear problems in [33]. SVMs have been frequently used for the classification of political texts [12, 13, 18]. They are some- times seen to be the classification method to be used for short texts in social sciences [8]. Unlike the LR method which provides a probability as its output, the output of an SVM gives the direct prediction of the label. SVM classification works by fitting a hyperplane that separates the classes and maximizes the separation margin [32]. Support vectors refer to those samples that are the closest to the

separating hyperplane. These are the only samples that impact the SVM training, as they are most likely to cause misclassification. The margin is given by the length of the projections of the supporting vectors onto the vector of parameters w which is perpendicular to the separat- ing hyperplane. The average loss is given by Eq (1) with a hinge loss function. Fig 1. Illustration of a logistic regression classifier. The sigmoid-based surfaces shows the probability of the positive class given the observed data. https://doi.org/10.1371/journal.pone.0290762.g001 PLOS ONE Short text classification with machine learning in the social sciences PLOS ONE | https://doi.org/10.1371/journal.pone.0290762 September 29, 2023 6 / 26