Emotion analysis is one of the new branches of AI and natural language processing which goes beyond whether the context of a text piece is positive, negative or neutral (i.e. sentiment analysis). Instead emotion analysis tries to identify the exact emotions (e.g. happiness, disappointment, etc.). This has been proven helpful for businesses in detecting emotion in the received feedback which leads to a better understanding of needs and goals.
To have an example of emotion analysis, imagine a dataset composed of clients' feedback. A sentence such as
We stopped calling as there was never an answer can be marked as
It was exactly what we hoped for can be considered as
How can emotion analysis be beneficial
Emotion analysis is the process of identifying the underlying emotions expressed in textual data. This is beneficial to businesses to understand emotions expressed by their customers and staff and the reasons behind them.
Relevance AI provides you with a no-code workflow to analyse the emotion of text fields in your dataset. This is done via complex and state-of-the-art neural networks trained and tested for this specific task.
Once you have uploaded your data, select your dataset, click on Extract emotion under Workflows and follow the instruction.
Clicking on "Get started" and "Continue" will activate each relevant section.
- specify the field you want to analyse
- select your preferred model for emotion analysis
- type a name for a new column under which the results are automatically added to your dataset
- Execute the workflow
When the workflow is finalized, go to Datasets and you will see the new field is added to your original data with emotion tags corresponding to the analysed field.
Updated about 4 hours ago