Boosting classification reliability of NLP transformer models in the long run

Transformer-based machine learning models have become an essential tool for many natural language processing (NLP) tasks since the introduction of the method. A common objective of these projects is to classify text data. Classification models are often extended to a different topic and/or time period. In these situations, deciding how long a classification is suitable for and when it is worth re-training our model is difficult. This paper compares different approaches to fine-tune a BERT model for a long-running classification task. We use data from different periods to fine-tune our original BERT model, and we also measure how a second round of annotation could boost the classification quality. Our corpus contains over 8 million comments on COVID-19 vaccination in Hungary posted between September 2020 and December 2021. Our results show that the best solution is using all available unlabeled comments to fine-tune a model. It is not advisable to focus only on comments containing words that our model has not encountered before; a more efficient solution is randomly sample comments from the new period. Fine-tuning does not prevent the model from losing performance but merely slows it down. In a rapidly changing linguistic environment, it is not possible to maintain model performance without regularly annotating new text.

The presence of occupational structure in online texts based on word embedding NLP models

Research on social stratification is closely linked to analysing the prestige associated with different occupations. This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data. The results are compared to standard results in social stratification to see whether the classical results are reproduced and if additional insights can be gained into the social positions of occupations. The paper gives an affirmative answer to both questions. The results show fundamental similarity of the occupational structure obtained from text analysis to the structure described by prestige and social distance scales. While our research reinforces many theories and empirical findings of the traditional body of literature on social stratification and, in particular, occupational hierarchy, it pointed to the importance of a factor not discussed in the main line of stratification literature so far: the power and organizational aspect.