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.
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. Here, we explain the top two ways to access sentiment analysis on Relevance AI.
AI Clustering workflow is one of the hero workflows at relevance AI. Under a few steps, you can select to activate not only clustering but also Sentiment analysis. Results will be added to your dataset under their own independent fields (i.e. sentiment independent of clustering).
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, under the Data view page.
See Emotion workflow if you are after more detailed analysis on your data.
Updated about 1 month ago