Content Comparison

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

Sentiment	Explanation	Sentiment rate
`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%]

Note
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",

Info

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:

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.

Note
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.

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	Best practices

for creating themes and ensuring the relevance of

if you are manually adding themes to ensure effective results:
	We strongly recommend that you do not use keywords in addition to the AI to recognise themes.
	We strongly recommend using short themes that encapsulate one idea at a time.

We never use our customers' data to train the AI used (except in the case of a specific request in the "Feedier NLP" contract). The models are pre-trained upstream.
No customer data is saved outside the Feedier infrastructure when AI is used.
AI-related functionalities can be deactivated in Feedier, free of charge, on request to the Feedier support team(support@feedier.com).

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