Affect Enriched Word Embeddings for News Information Retrieval
Table 1: Mean affect scores on some common IR datasets
['Dataset affect scoring Dataset', 'Dataset affect scoring formality', 'Dataset affect scoring politeness', 'Dataset affect scoring frustration']
In order to assess the potential applicability of Aff2Vec embeddings in the context of information retrieval, we run preliminary evaluation of the amount of formality, politeness and frustration contained in common text collections used in information retrieval experiments. For this purpose we leverage the affect scoring algorithm that is used for building Aff2Vec embeddings. We extract mean affect scores for formality, politeness and frustration on each dataset. Such an evaluation involves two collection of news: the datasets from TREC core 2018 track, Washington Post articles, and TREC core 2017 track, New York Times articles.