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Advanced Analytics for Operationalization of Textual Data

Advanced Analytics for Operationalization of Textual Data
文本数据操作化的高级分析
批准号:
570843-2021
负责人:
Ruhe, GuentherGH
金额:
$3.64万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

项目摘要

项目成果

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中文摘要
翻译
挑战:为了根据非定量数据做出决策,我们一直依赖于软技能,如头脑风暴,谈判和利益相关者研讨会。随着从传统的客户关系管理向平台中介、在线和开放的用户反馈的转变,出现了大规模和多样化的信息,这些信息通常以评论、帖子、评论、讨论的形式出现,这将组织间的谈判和头脑风暴转变为用户和反馈驱动的产品管理。目标:该项目的目标是为文本数据分析提供一个平台,支持产品和服务决策的三个阶段。重点是提供按需云服务,以支持执行层面的战术和战略决策。要做的努力:决策是一系列分析的结果。这些分析的选择、顺序和粒度对于任何专家来说甚至事先都不清楚。我们提供模块化的云服务,专家可以选择和应用不同级别的任何分析集。这些服务将联合收割机与现有和提取的知识结合起来,并增强了自然语言处理、深度学习和情感分析的现有组件。在隔离的组件之上,设计了一个集成层,包括选择和进行的分析之间的映射,以及在产品和服务开发的背景下对战术和战略决策的映射。影响:(阿尔伯塔)中小企业在创新道路上遇到了障碍,无法获得先进的数据管理和分析解决方案。他们通常既负担不起数据科学家,也没有可用的数据科学家。随着阿尔伯塔的多样化和全球化,可以从各种分布式源访问非结构化文本数据。这些数据有助于企业更好地了解客户需求,创造新的产品和服务创新,并监控和控制其开发流程。
英文摘要
CHALLENGE: For making decisions based on non-quantitative data we have been relying on soft skills such as brainstorming, negotiation, and stakeholders workshop. With the shift from traditional customer relationship management to the platform mediated, online and open user feedback there is a large scale and diverse information often in the form of comments, posts, reviews, discussions which is shifting the process from inter-organizational negotiation and brainstorming into the user- and feedback-driven product management. GOAL: The Goal of this project is to provide a platform for Textual Data Analytics supporting the three stages of Product and Service decision-making. The focus is on providing on-demand cloud services to support tactical and strategic decisions on an executive level. EFFORT TO BE UNDERTAKEN: Decision-making is the result of a series of analyses. the choice of these analyses, the sequence, and granularity are not even clear in advance for any expert. We are providing a cloud service that is modular and the expert can choose and apply any set of analyses at different levels. The services combine and enhance existing components for Natural Language Processing, Deep Learning, and Sentiment Analysis with existing and extracted knowledge. On top of the isolated components, an integration layer is designed that includes the mapping between the analysis chosen and made and the mapping towards tactical and strategic decision-making in the context of product and service development. IMPACT: (Alberta) SMEs are roadblocked in their innovation pathway without getting access to advanced data management and analysis solutions. They often can neither afford the data scientists nor they are available. As Alberta diversifies and due to globalization, unstructured textual data is accessible from various distributed sources. This data helps companies understand customer needs better, create new product and service innovations, and monitor and control their development processes.
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