Text classification and sentiment analysis are related fields, text classification not only focuses
on sentiment, but also includes the processing of all texts and tasks. The work of Liang et al.
[107] showed that GLM-130B was the best-performed model, with an overall accuracy of 85.8%
for miscellaneous text classification. Yang and Menczer [220] found that ChatGPT can produce
credibility ratings for a wide range of news outlets, and these ratings have a moderate correlation
with those from human experts. Furthermore, ChatGPT achieves acceptable accuracy in a binary
classification scenario (AUC=0.89). Peña et al. [145] discussed the problem of topic classification
for public affairs documents and showed that using an LLM backbone in combination with SVM
classifiers is a useful strategy to conduct the multi-label topic classification task in the domain of
public affairs with accuracies over 85%. Overall, LLMs perform well on text classification and can
ASurveyonEvaluationofLargeLanguageModels