  Fine-grained financial sentiment analysis on news headlines is a challenging
task requiring human-annotated datasets to achieve high performance. Limited
studies have tried to address the sentiment extraction task in a setting where
multiple entities are present in a news headline. In an effort to further
research in this area, we make publicly available SEntFiN 1.0, a
human-annotated dataset of 10,753 news headlines with entity-sentiment
annotations, of which 2,847 headlines contain multiple entities, often with
conflicting sentiments. We augment our dataset with a database of over 1,000
financial entities and their various representations in news media amounting to
over 5,000 phrases. We propose a framework that enables the extraction of
entity-relevant sentiments using a feature-based approach rather than an
expression-based approach. For sentiment extraction, we utilize 12 different
learning schemes utilizing lexicon-based and pre-trained sentence
representations and five classification approaches. Our experiments indicate
that lexicon-based n-gram ensembles are above par with pre-trained word
embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific
BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%.
Further, using over 210,000 entity-sentiment predictions, we validate the
economic effect of sentiments on aggregate market movements over a long
duration.
