Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM.

ABSTRACT: Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes

SUBMITTER: Liu JX 

PROVIDER: S-EPMC10280432 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature