Sentiment refers to the overall tone emerging from a post. On Radarly, it allows you to classify words according to 4 different sentiments: positive, negative, neutral, and mixed.
This allows you to understand the sentiment expressed on a brand, a product, or a subject and more precisely the feelings of your community.

The sentiment of the caption is detected automatically thanks to machine learning. It is based on language as well as emoticon/emoji detection. Different algorithms are involved in sentiment analysis, and this analysis is developed from statistical methods of supervised learning (Machine Learning). These algorithms are called classifiers because they aim to classify each post in one of the four allowed tones: positive, negative, neutral, and mixed.

Note
When the algorithms fail to detect the sentiment of the publication, the "neutral" sentiment is applied by default.

From the search and filter bar, in the section Main filters > Sentiment then select the sentiment of your choice.

Net Sentiment is the overall sentiment balance:

Net Sentiment = % of positive posts - % of negative posts

It makes benchmarking by sentiment easier, you can use it in the bubble chart or the KPI card when building your own Insight Pages.

It is possible to add a layer of "semi-automated" treatment thanks to tagging (tone codifications in the presence of specific words).

It is also possible to manually tag the sentiment of the posts. The tagging of publications can thus be supplemented by an intervention of a user if the sentiment does not seem suitable for the content.

💡 Tip

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