Summarization

It takes a lot of effort to determine what a text document is truly about, even when it is well-organized and searchable, and the majority of longer texts don’t come with a convenient TL;DR or summary. Fortunately, AI is rapidly improving at understanding the key ideas the author was attempting to convey and at generating a summary of the text itself.

The classification of the text by topic allows us to quickly search through the documents, while summarization allows us to quickly grasp the information contained in the text. Everyone who has to deal with large volumes of text, from analysts to executives, will benefit greatly from this.

Sentiment analysis

Languages are very nuanced, so while some of the information is factual, like a judgement that the restaurant’s food was bad, other parts are emotive, like the man’s outrage about the food’s quality.
Machines are generally not very good at reading emotions, but with the aid of AI, they can pick up cues that indicate the sentiment attached to a segment of text, showing whether the content is happy, sad, or evokes any other sentiment the model is trained to recognize.
This allows our clients to better understand their customer feedback, adjust chatbot responses, and react to prevailing sentiments regarding specific topics reflected in the comments. It has also been used to understand sentiment concerning specific topics in the media and on social media. In this case, NLU and sentiment analysis are used in tandem to recognize topics of interest and subsequently interpret sentiments about them.