Based on the news of listed enterprises and the open public data, the Bd quantitative financial analysis system can make a quantitative evaluation for listed enterprises from the dimensions of public opinion and sentiment and can work out the attribution of public opinions based on the contents of messages objectively.
The algorithm is based on the data obtained from public channels and is uniform, without any human intervention to the evaluation process and results. It is aimed at being objective and fair to all evaluation objects as much as possible.
*Note: Due to integrity and promptness of data and accuracy of the algorithm, the evaluation result might be inconsistent with the real conditions and is for reference only. The evaluation result is not the view or standpoint of Global Tone Communication Technology Co., Ltd. If you have any objections to the evaluation results of the product, please contact us via【firstname.lastname@example.org】.
1. Sentiment algorithm:
According to the enterprise information obtained from the public channels (including news and social media platforms), such as product information, commercial cooperation and change of officer, the automation algorithm is used for deeply processing the enterprise information, and analyzing and calculating the same through multiple factors such as objective sentiment factors, content of information, enterprise correlation and attenuation time of the information. By virtual of combining all information released by the news and the social channels for the whole same day and combining the assigned media level weight, the final score is worked out.
*As the label is made by the sentiment algorithm based on the corpus, the objective content will be mainly classified by sentiments. After multiple manual rechecks are done and the extreme labels are removed, we use the machine learning and training mode. However, the scoring results might be impacted.