Abstract:
Sentiment analysis helps data analysts to find public opinion, actual meaning of
the given text (positive meaning, neutral meaning or negative meaning) conduct market
research, monitor brand and product reputation, and understand customer experiences of
newly introduced items or service.
Stock news sentiment analysis is a useful task in the financial domain. However,
this is different from the customer feedback for a product or brand, movie review and
customer support reviews. This huge difference is because of the domain specific
language in stock markets and lack of labeled data. This research implements a stock news
sentiment analysis system using the latest transformer-based pre-trained language models
in NLP. I could get higher sentiment classification results for the transformer-based pretrained
language
models
than
the
traditional
classifications
models
in
this
research.
Also
I
could reduce classification result bias for the particular stock market specific words,
because of the transfer learning method. And I could introduce correlation between stock
news sentiment and stock price change percentage value. This proposed model can predict
the percentage change value of the stock when received a news.
Citation:
Kaushalya, W.A.S. (2022). Sentiment analysis of financial stock market news using pre-trained language models [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. hhttp://dl.lib.uom.lk/handle/123/22519