Text Analytics

Based on Artificial Intelligence technologies, QWAM Text Analytics makes it easy to process and analyze large amounts of text (unstructured content) to capture key elements and indicators.

QWAM Text Analytics meets the following uses and needs:

Sales, Market and Business Intelligence, Analytics of Collaborative Reviews, Customer Service, Analytics of Surveys and Studies with Open Questions, E-Reputation, Human Resources, Media and Content Editing (SEO, Contextualization, Meta-Data Generation, Content Creation Help…).

The challenge of analyzing unstructured content

Web development and the digital transformation of enterprises have led to an explosion of textual data that is largely unstructured or poorly structured:
  • on the web – site pages, news articles, comments, social media posts, customer reviews, etc.
  • within organizations – reports, studies, regulatory documents, contracts, internal investigations and social networks, etc.
  • online platforms for various surveys and opinion books
  • customer reviews and satisfaction questionnaires on e-commerce platforms
QWAM recognizes the economic challenges of mastering text data mining and analysis technologies. These issues concern all sectors of the economy and the business lines, as part of their digital transformation projects. Thanks to major investments, including ambitious research programs in Artificial Intelligence, we have developed QWAM Text Analytics.

QWAM Text Analytics: What to do?

Analysis of spontaneous expressions (customer reviews, opinion polls, etc.)

Opinion polls, customer reviews, employee feedback…all of these exchanges are happening today through digital media. QWAM Text Analytics enables automation of the processes required to analyze text within business contexts.

QWAM Text Analytics enables analysis of the themes of each survey or set of open-ended question answers (verbs) and automatic processing of suggestions, recommendations, or other feedback from respondents.

QWAM also offers a powerful sentiment analysis module that takes into account the business context. This module allows you to benefit from a fine analysis of the feeling and a reliable evaluation of the tonality of the expressions collected.

Enhancing content by adding metadata

The digital transformation of companies often begins with a digitization of their data.
QWAM Text Analytics – with pull modules – enables efficient characterization of each document through metadata calculation. This makes it easy to build a “facet search engine,” with each piece of metadata seen as one of those facets. The search for documents can therefore be carried out in successive stages: from a simple query, the user can focus on a subset of documents meeting certain criteria (facets) and then specify his search. Similarly, automate document ordering through extracted characteristics.
QWAM Text Analytics enables such processes by customizing metadata to meet business and application needs.

Mining and analyzing unstructured data sets

For large bodies of text data QWAM Text Analytics groups documents according to specific criteria (customer requirements) and extracts key information. It is then possible to construct new data from the extracts that are made and to identify the relationships between the extracted entities (for example, to show that a person is related to a set of companies). This way the texts are organized and accessible in coherent subsets and according to your business issues via dashboards and/or a search engine.

Qwam Text Analytics: How does it work?

QWAM Text Analytics is built around three main components that will perform all of the proposed processing:

  • The “extraction engine” that will detect from specific rules and processes, company names, people, more general concepts, …
  • The discovery engine that provides document aggregation, semantic enrichment such as sentiment analysis, or detection of topical concepts for a given domain.
  • The screening module that allows an analyst to monitor the results of the first two engines and, if necessary, correct them.

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Customer Testimonials

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