So far, studies seeking to recognise moral foundations in texts have been relatively successful (Araque et al., 2019; Lin et al., 2018; Mooijman et al., 2017; Rezapouret al., 2019). There are, however, two issues with these studies: Firstly, it is an extensive process to gather and annotate sufficient material for training. Secondly, models are only trained and tested within the same domain. It is yet unexplored how these models for moral foundation prediction perform when tested in other domains, but from their experience with annotation, Hoover et al. (2017) describe how moral sentiments on one topic (e.g. black lives matter) might be completely different from moral sentiments on another (e.g. presidential elections). This study attempts to explore to what extent models generalise to other domains. More specifically, we focus on training on Twitter data from non-extremist sources, and testing on data from an extremist (white nationalist) forum. We conducted two experiments. In our first experiment we test whether it is possible to do cross domain classification of moral foundations. Additionally, we compare the performance of a model using the Word2Vec embeddings used in previous studies to a model using the newer BERT embeddings. We find that although the performance drops significantly on the extremist out-domain test sets, out-domain classification is not impossible. Furthermore, we find that the BERT model generalises marginally better to the out-domain test set, than the Word2Vec model. In our second experiment we attempt to improve the generalisation to extremist test data by providing contextual knowledge. Although this does not improve the model, it does show the model’s robustness against noise. Finally we suggest an alternative approach for accounting for contextual knowledge.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-426921 |
| Date | January 2020 |
| Creators | van Luenen, Anne Fleur |
| Publisher | Uppsala universitet, Institutionen för lingvistik och filologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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