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Text Analysis guide

Text Analysis guide

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Text analysis is the ability to leverage AI to analyze automatically all text/unstructured data in your Feedier account.

The module offer different features out of the box, such as:

  • Automatically generating summaries

  • Automatically detecting new topics

  • Tracking and analyzing topics (with sentiment analysis and link to attributes)

  • Follow topic trends

  • Leverage Feedier’s filtering technology as in the Dashboards, Reports, Segments, etc.


How to set up Text Analysis?

Text analysis must be enabled from an administrator user in the “Roles & Capabilities“ page.

Data is analyzed by the Text Analysis module

The module is automatically using all text/unstructured data present in the teams the user has access too. So, as long as the account has open text questions, Text analysis will bring value and show automatically new topics and measure sentiment.

Topics in Feedier

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A topic in the Feedier Platform is a way to group different answers/text based on a common meaning.

Topics in the Feedier Platform can be:

  • Automatically suggested by Eureka AI or created manually from a Feedier user

  • Tracked over time

  • Organized in sub topics

  • Summarized

  • Explored through its answers, linked topics and linked attributes

It’s important to note that:

  • Topics can be given specific instructions based on your needs in the Edit section

  • Topics are by default automatically attached by Feedier, but they can be detached or attached manually by a User.

Sentiment analysis in Feedier

After evaluating the topics, Feedier will automatically assign a sentiment score from 1 to 100% based on the overall sentiment of the text.

For the sake of the simplicity, the score is displayed with a label:

  • Positive From 71% to 100%

  • NEUTRAL From 31% to 70%

  • Negative From 1 to 30%

From Text Analysis to actionable Insights

From global to specific

The main important feature of the Text Analysis module is the ability to filter in real-time all the insights based on your specific context (time frame, attributes, source, team, etc.).

Generated topics and summaries will be more valuable when narrowing down the scope with the Feedier filter technology.

Detecting pain points

Two elements are extremely important when it comes to pain points in Text Analysis:

  • The painpoint itself

  • Its impact on the business

Feedier helps you do to both in one place.

Identifying pain points

There are two different methods:

  1. Use global topic names (such as product, quality, service, sales, etc.) and add a sentiment score filter (positive or negative) to only match outliers in the Text analysis module.

  2. Use specific topic names (such as product bugs, product ideas, service issues, sales bottlenecks) to only match outliers.

In both cases, Feedier will be able to identify clearly the pain points for you.

The impact of every single topic can be found on the left panel with its volume over time:

Indentifying root cause

For every topic, Feedier provides a comprehensive view with information such as:

  • Feedback

  • Related attributes

  • Related topics

When it comes to root cause, the attributes related to the topics are the most actionable insights. It means that for every topic (based on the filters set), you can easily identifty common similarities.

Creating Action Plans

Action plans with key improvement and pain points can be generated for every single topic by Feedier.

Leveraging Feedier reports

Insights coming from topics and text answers can be integrated with the Feedier Report module.

It’s possible to easily display:

  • Top X topics or answers based on sentiment

  • Top X topics or answers based on volume

Last but not least, every widget in the report can be filtered based on time, source, attribute or any other filter.

FAQ

Topics are organized per teams. A parent team will see topics from all sub teams.

Feedier does not use client data for AI model training. We use a technology called RAG (retrieval augmented generation) to leverage real-time feedback data, organization context and feedback from the user to have the best topic evaluation system.

Whenever a feedback is centralized in Feedier, the Platform will evaluate for every single topic attached to the feedback’s team (and parent teams) its potential topics.

The evaluation takes into consideration:

  • The instructions (if any) attached to the topic

  • The answers that were attached or detached manually by users

When opening a topic in the Text Analysis module, Feedier will display the topic author and its creation date.

Yes. Feedier uses the parent topic in the sub topic evaluation. So, if you have
- Quality
— Product
- Ideas
— Product
Feedier will show different results for both Product topics.

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