Intelligent Enterprise Knowledge Base Solution
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Chat BI SolutionAssisting Investment Decision-making Through Fine-grained Public Opinion Analysis
Business Scenarios and Pain Points
Pain Points
01
Low Accuracy Rate of Sentiment Judgment Based on Keywords
Traditional algorithms judge the sentiment of articles based on keywords and rules, it can not consider word segmentation and context, and lacks the ability to understand semantics.
Pain Points
02
Emotional Judgments Do Not Align with Business Needs
The public opinion analysis data provided by existing service providers generally suffers from problems such as inaccurate judgment and insufficient negative discrimination. Moreover, direct business-oriented demand feedback is also difficult to optimize and adjust in a targeted manner.
Pain Points
03
Lots of Repetitive Information Creates Noise
In order to perceive important public opinion in the first place, enterprises often need to access multiple information sources at the same time. There are a large number of mutually reprinted articles among various information sources, which brings a heavy burden to business personnel to monitor public opinion.
Pain Points
04
Objects That Cannot Be Identified
Traditional public opinion analysis techniques can only use keywords and rules to judge sentiment at the text or sentence level, it can not make accurate judgments when multiple target companies are mentioned at the same time.
Financial Public Opinion Analysis Solution
Emotion Understanding Based on Semantics and Domain Knowledge
A true understanding of rich and varied language and domain expertise
Based on the Mengzi pre-training model technology system, text emotions can be understood from a semantic perspective combined with context. Through the study of massive professional basic corpus in the financial field and the judgments of front-line public opinion analysts, the accuracy and professionalism of public opinion judgments have been greatly improved.
Multi-granularity and Comprehensive Analysis of Public Opinion
Comprehensive analytical capabilities from chapters, paragraphs, to corporate entities
Using the context understanding ability and abstraction ability of Mengzi pre-trained language model technology, the target article can be analyzed from multiple angles. Cooperating with corporate entity identification and chain index, it can accurately identify and correlate tens of thousands of listed bond-issuing companies, and accurately locate the different emotions for each corporate entity in the intricate description.
Massive Article Intelligent Deduplication
Efficiently locate important public sentiments in massive data
Through the Mengzi pre-training language model technology, it is possible to judge whether the description content is repeated based on the abstract understanding of the content of the article, and effectively identify the slightly rewritten repetitive articles that are difficult to distinguish using rules. It prevents business personnel from being overwhelmed by a large number of repeated messages, thus unable to pay attention to important public opinions on time.
Business Value
Effectively Improve the Accuracy of Negative Public Opinion Identification and Classification
In terms of the accuracy of public opinion classification, the engine helps each downstream business system to accurately identify multi-level public opinion information, effectively improves the distinction between public opinion at each level, and provides more fine-grained decision-making support signals for the business side.
Filtering Articles from Companies that are not the Main Target of Public Opinion
The main body of the company concerned by business personnel is often mentioned in a large number of articles, but most of the time it does not appear as the main object of public opinion. By analyzing the description content of the full text, whether it is the main object of public opinion can be judged, and a sentiment analysis for this company can be provided.
Reducing Time Spent Manually Processing Repetitive Information
With the development of big data technology, the number of public opinion information sources accessed by financial institutions has reached tens of thousands. Through massive information deduplication technology, articles with repetitive content can be largely filtered to reduce noise interference by business personnel.
Application Cases
China AMC
The public opinion system realizes processing capabilities such as public opinion subject extraction, subject-oriented emotion recognition, and semantic-based article content deduplication, and can perform multi-level sentiment judgments on public opinion subjects, quickly locate the company subject of concern from massive public opinion information. The positive and negative information is stored in a unified data platform and provided to multiple downstream business systems. At the same time, through the pre-training model domain adaptation and model lightweight technology, a large amount of computing resources are saved under the premise of ensuring excellent results.
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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.
© 2023, Langboat Co., Limited. All rights reserved.
Large Model Registration Code：Beijing-MengZiGPT-20231205
Business Cooperation:
bd@langboat.com
Address:
Floor 16, Fangzheng International Building, No. 52 Beisihuan West Road, Haidian District, Beijing, China.
Official Accounts:
© 2023, Langboat Co., Limited. All rights reserved.
Large Model Registration Code：Beijing-MengZiGPT-20231205
