TY  - JOUR
A1  - Wilksch, Moritz
A1  - Abramova, Olga
T1  - PyFin-sentiment
T2  - International journal of information management data insights
N2  - Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discussing the topics of finance and investing. We manually assign each tweet its market sentiment, i.e., the investor’s anticipation of a stock’s future return. Using this data, we show that all existing sentiment models trained on adjacent domains struggle with accurate market sentiment analysis due to the task’s specialized vocabulary. Consequently, we design, train, and deploy our own sentiment model. It outperforms all previous models (VADER, NTUSD-Fin, FinBERT, TwitterRoBERTa) when evaluated on Twitter posts. On posts from a different platform, our model performs on par with BERT-based large language models. We achieve this result at a fraction of the training and inference costs due to the model’s simple design. We publish the artifact as a python library to facilitate its use by future researchers and practitioners.
KW  - sentiment analysis
KW  - financial market sentiment
KW  - opinion mining
KW  - machine learning
KW  - deep learning
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/62538
SN  - 2667-0968
VL  - 3
IS  - 1
PB  - Elsevier
CY  - Amsterdam
ER  -