TextminingAnalyse de Données pour la Qualité (ERE)

In a context where the user is no longer a mere passive user of products and services but an actor building his uses according to sources of interest, the aim of this module is to acquire tools and methods for analysing the signs and codes that are expressed.

Know: • Differentiation of Text Data Analysis (ADT) types of approaches: choice of analysis/choice of tools • Proxem discovery to monitor social networks and the web: e-regulation Master: • Rules used to interpret texts written in natural language • Verbatim analysis techniques on large volumes of information: telephones, e-mails, mail and other • Text analysis software: TROPES software

Introduction • The different evolutions of text analysis and semantic analysis • Semantic analysis, queen of textual data analysis • The limits of semantic analysis

Sequence 1: • What is text analysis and why use it? o From gross data to useful information Data and sources for text analysis Text analysis, a way to listen to the client

o Make sense of useful information Different approaches to analysis of textual data From textual data analysis to semantic analysis Semantic analysis to interpret client feelings

o Text analysis at the service of the company Meet needs and meet new customer needs Enterprise e-reputation and competitive intelligence Productivity gains through customer experience analysis

Sequence 2: • Text analysis tools: Tropes, Proxem, etc... o The various tools available: from text analysis to semantic analysis o Measuring the performance of different semantic systems

Sequence 3: • Applications of semantic analysis o Semantic analysis, an indispensable tool of customer experience management What the client does What the client says What the client thinks o Semantic analysis to react Product adaptation: case study Adapting customer listening: case study Crisis management: a case study

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