Sentiment analysis is one of the new branches of AI and natural language processing with the goal of understanding whether the context of a text piece is positive, negative or neutral. This has been proven helpful for businesses in detecting sentiment in the received feedback which leads to a better understanding of needs and goals.
To have an example of sentiment analysis, imagine a dataset composed of clients' feedback. A sentence such as
I am tired of the delays is a
I appreciate the time you spend in the support line is a positive sample.
How can sentiment analysis be beneficial
Sentiment analysis focuses on the polarity of a text (positive, negative, neutral) and has been proven helpful for businesses in detecting sentiment in the received feedback which leads to a better understanding of needs and goals.
Relevance AI provides you with a no-code workflow to analyse the sentiment 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 Sentiment under Workflows and follow the instruction.
Clicking on "Get started" and "Continue" will activate each relevant section.
- specify the field you want to analyse
- type a name for a new column under which the results are automatically added to your dataset
- Execute the workflow
After the workflow is finalised you can view the results (sentiment tag and sentiment score) under new fields that are automatically added to your dataset, on the Data view page.
We will learn about the Emotion workflow on the next page.
Updated about 1 month ago