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Why does Feedier use artificial intelligence?

In the Feedier Platform, some of the data processed is unstructured data (textual feedback). Analysing this data can be tedious.
Today, Feedier uses artificial intelligence (AI) to carry out 3 types of analysis:
1: Sentiment analysis
2: Entity detection
3: Theme (topic) detection

General configuration

Feedier AI uses the context of the business to enable our models to deliver results that make sense in the operational context.

  • Go to your organisation page(Admin role)

  • Add an organisation name

  • Add a description of the organisation

 

Screenshot 2024-01-10 at 10.56.02.png

It is important to complete the Description field on the "Organization" page. This description must be short and general. It can come directly from the website or the Wikipedia page.

Type 1 : Sentiment analysis

Each verbatim analysed is assigned to a sentiment: Positive, Neutral ou Negative.

Sentiment

Explanation

Sentiment rate

(smile) Positive

The verbatim is linked to a positive emotion on the part of the customer: Joy, Confidence, Serenity, Admiration.

[95%-100%]

😐 Neutral

The verbatim is not directly linked to an emotion or the emotion is not expressed strongly enough to be categorised.

50%

😠 Nagative

The verbatim is linked to a negative emotion on the part of the customer: Anger, Contempt, Sadness, Disgust.

[1%-5%]

The verbatim is analysed along with the question. It is therefore important to ask the right question to get a meaningful analysis.
Examples of bad questions: "Question Texte", "Q Texte", "Avis",

How is the sentiment rate calculated for a theme?

This is the average of the sentiment scores for each verbatim related to a theme.

Here are a few examples:

(smile) Number of positives

10

10

15

5

😐 Number of neutrals

0

10

10

10

😠 Number of negatives

0

10

5

10

Sentiment rate

~ 100%

~ 50%

~ 66%

~ 40%

Type 2 : Entity detection

With Feedier, the "entities" extracted from the verbatims are added as attributes.

Attribute

Explanation

Nlp Personal Name

The value corresponds to a personal name. The value can contain the surname, first name or both.

Nlp Brand

The value corresponds to the name of a company in the public domain.

Nlp Product

The value corresponds to company products that are not necessarily in the public domain, but are detected as being products mentioned by the customer.

Nlp Location

The value corresponds to a physical location.

The models may not detect 100% of entities and/or there may be a margin of error. Please do not hesitate to pass on any such information to the support@feedier.com team.

The labels (eg. Nlp Brand) displayed on the dashboard can be changed from the Attributes page.

Type 3 : Theme (topic) detection

Themes allow verbatims to be grouped according to common subjects. Once you have created a theme, you can:

  • Track the development of themes

  • Organise themes by sub-theme

  • Filter themes using the different Feedier filters

 

Best practices if you are manually adding themes to ensure effective results:

(tick)

We strongly recommend that you do not use keywords in addition to the AI to recognise themes.

(tick)

We strongly recommend using short themes that encapsulate one idea at a time.

Only administrators and editors can create/modify/delete a theme.

Choose topics that you’d like to track, if available, Feedier will group text responses that would fit under this topic type.

If you click on view the topic, you will only see the words of your topic appear in the ranking table, keyword cloud and text answers.

Topics will allow you to look for certain group of words and themes. For example, you can create a topic on Product Bugs and add words related to that topic. 

If you want to look for a group of words, you can add the signs && in the search. To look for company excellence, you need to type in company&&excellence. The search will be showing every comment that contains the word company AND excellence

You can always go back to add or remove words from the topic. 

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