Automatically Summarize million (or more) users/clients (change) requests of Apps/Products/Tools using Text Summarization approaches
GOAL: The goal of this project is to automatically recognizes natural language paragraphs reporting “the essential feedback/ideas written in user/clients reviews” that can be relevant for evolving our products, applications, tools, etc.. The purpose of this project is to capture informative client/user reviews (among thousands or millions or user/client reviews) and consequently to allow managers/developers to better manage the information contained in user/client reviews.
CHALLENGES AND RISKS: natural language paragraphs can be extracted according to a taxonomy of aspects (or categories of feedback/ides) that are important in an Organization for improving its products, applications, tools, etc. The categories can be easily customized depending on the company needs. The only risk of the project is that some categories cannot be relevant for other projects, so the main challenges is to make the tool enough general that it works for a wide range of applications.
HOW DO IT: The tool will merge three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify user/client reviews into the proposed categories.
GOAL: The goal of this project is to automatically recognizes natural language paragraphs reporting “the essential feedback/ideas written in user/clients reviews” that can be relevant for evolving our products, applications, tools, etc.. The purpose of this project is to capture informative client/user reviews (among thousands or millions or user/client reviews) and consequently to allow managers/developers to better manage the information contained in user/client reviews.
CHALLENGES AND RISKS: natural language paragraphs can be extracted according to a taxonomy of aspects (or categories of feedback/ides) that are important in an Organization for improving its products, applications, tools, etc.
The categories can be easily customized depending on the company needs. The only risk of the project is that some categories cannot be relevant for other projects, so the main challenges is to make the tool enough general that it works for a wide range of applications.
HOW DO IT: The tool will merge three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify user/client reviews into the proposed categories.