Mining ESG Information in Massive Text Data to Assist Investment Decision-making
Business Scenarios and Pain Points
Pain Points
01
Low Disclosure of ESG Information by Domestic Companies
Only a small number of companies publish formal ESG reports. The disclosure content is often incomplete, and it is difficult to guarantee authenticity. Investors need to collect and analyze relevant public information on the Internet in order to make an objective and timely evaluation.
Pain Points
02
The ESG Information System is Complex and Has Many Indicators
ESG analysis frameworks of different institutions often have different specific standards. Dozens of major topics under the three major areas can be further disassembled into hundreds of information points. There may be differences in data collection and scoring standards for each information point.
Pain Points
03
Inefficient Manual Analysis of ESG Information
Due to the low degree of information disclosure, scattered data sources, and complex system indicators, the collection and analysis of ESG information for only one company will consume a lot of manpower. Relying entirely on manual collection and analysis is difficult to meet the needs of investment.
Pain Points
04
Dispersed Source of ESG Information
In addition to specialized ESG reports, ESG information required for investment analysis also has many important data sources, such as: corporate annual reports, media information, government documents, social platforms, recruitment websites, etc.
ESG Investment Analysis Solution
Next Generation Semantic Search Technology
Precisely locate ESG information in massive text data
Through semantic search and knowledge graph technology, it is possible to conduct accurate paragraph-level information searches in various data sources, accurately find the ESG information points that investors care about, and perform information summary and index according to the customized ESG analysis system .
Customizable Information Extraction Technology
Flexible definition of any information points you want to extract
Traditionally, manual or keyword and rule methods are used to extract specific data corresponding to hundreds of information points with low efficiency and unsatisfactory coverage. Through Langboat Finance's customizable information extraction technology, the generalization ability of the pre-trained model can be used to flexibly respond to changing information extraction requirements, effectively improve the coverage of information extraction, and reduce development costs.
Multi-Granular Sentiment Analysis
Automatically analyze the positive and negative sentiment of ESG information, assisting investment rating
Diversified ESG information sources often contain a large amount of descriptive text, which can not be scored through numerical values and rules. Through Mengzi pre-training language model technology, it is possible to conduct multi-granularity semantic sentiment analysis on ESG information points, distinguish positive and negative emotional expressions from texts, sentences to subject objects, and provide a basis for rating ESG information points.
Business Value
Quickly Extract Information
It can automatically process massive text data sources, support automatic information indexing and summary display according to the customized ESG analysis framework, and support information search through keywords. It can flexibly customize and expand more information extraction points, build a fully automatic analysis process, and greatly reduce the cost of manual information processing.
Accurately Evaluate Investment Risks
Through the leading semantic emotion recognition technology and real-time analysis of massive data, it helps investors discover negative ESG information in the first place, grasp the trend of emotional changes, and reduce investment risks.
Improve Investment Decision-making Efficiency
Through natural language processing technologies such as semantic search, text analysis, and information extraction, massive text data is mined and intelligently analyzed to provide investors with accurate, objective, and timely ESG investment decision support.
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Business Cooperation Email
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Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.
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