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. If it is not listed in the Overview page (i.e. under "Suggested workflows for this dataset"), one easy way to access a workflow is to search for it under Browse Workflows as shown in the image below.
Setup steps are:
- specify the fields you wish to analyse
- select your preferred model for emotion analysis. Models are different in the number of emotions they can detect
- type a name for a new column under which the results are automatically added to your dataset
- Optional: review the optional settings
- Change to No, if you do not wish to receive and email notification upon workflow completion
- Set up an advanced filter so the workflow only processes a subset of the data
- Click on "Run workflow"
When the workflow is finalized, go to Datasets and you will find the new field is added to your original dataset with emotion tags corresponding to the analysed field.
Note 1: You do not need to keep the page open while the workflow is in progress. You can close the window or explore other functionalities of the platform.
Note 2: You can run multiple workflows in parallel (i.e. no need to wait for one to finalize).
Note 3: Workflow results are saved back to the dataset.
Note 4: Workflow results are independent of each other meaning they do not overwrite each other unless a workflow is run twice with the exact same workflow setup.
Updated 4 months ago