The experiments found that investor sentiment in online reviews had a significant impact on stock yield. The experiments show that the Bert model used in this paper can achieve an accuracy of 97.35% for the analysis of investor sentiment, which is better than both LSTM and SVM methods....
A BERT pre-trained model for the financial domain would be specifically trained on a large corpus of financial documents, reports, news articles, and other relevant texts related to the finance industry. This specialized BERT modelwould be fine-tuned to understand the nuances of financial terminolog...
英文原文:LSTM vs BERT — a step-by-step guide for tweet sentiment analysis 标签:情感分类 Note from Towards Data Science’s editors:While we allow independent authors to publish articles in accordance with our 01 Image by Author Note from Towards Data Science’s editors:While we allow independen...
To address this challenge, in this paper, a novel sentiment analysis model for Chinese stock reviews based on BERT is proposed. This model relies on a pre-trained model to improve the accuracy of classification. The model use a BERT pre-training language model to perform representation of ...
The experiments found that investor sentiment in online reviews had a significant impact on stock yield. The experiments show that the Bert model used in this paper can achieve an accuracy of 97.35% for the analysis of investor sentiment, which is better than both LSTM and SVM methods....
COVID-19 has produced significant fluctuations and impacts on the Chinese stock market, and the sentiment analysis of stock reviews is important for the study of economic recovery. Owing to the shortage of well-annotated Chinese stock reviews, and the more emotional complexity and obscurity of ...
英文原文: LSTM vs BERT — a step-by-step guide for tweet sentiment analysis标签: 情感分类Image by Author Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution...
While the lexical approach needs a specific dictionary for the financial domain, algorithms based on machine learning need a database manually labeled by experts. In this sense, this work aims to propose an approach to forecast the Brazilian stock market using news headlines preprocessed through ...
Overall, we find that the market reacts to ESG news based on news sentiment. On the event day, positive ESG news has an average abnormal return of 0.31% while negative ESG news leads to a mean value of $$-0.75$$ - 0.75 %. More interestingly, we find that the impact of ESG news ...
Specifically, in [2], from a comparison of the 30 state-of-the-art methods for time series forecasting, as depicted in Table A1, a multivariate temporal convolutional network-based method exploiting sentiment analysis was proposed for the task of stock market forecasting. In addition, four more...