Hence, the word-level sentiment analysis offers all mobile number list a highly refined view of unstructured data blocks. Once you have this data pool, leverage it to improve your offerings and customer experience.
Use of Machine Learning for Sentiment Analysis
While the traditional software cannot associate individual words and phrases with a proper context, the advanced AI-powered sentiment analytics tools are equipped with contextual intelligence. Machine Learning powered analytics understand human emotions at more conceptual and relatable levels.
This proves highly beneficial in processing customer data gathered from social media where various emotions are pouring in every second.
Let’s take a few sentiment analysis examples.
The word “bloody” is generally associated with a negative sentiment.
Now, put it like this – “bloody awesome!!“; and the entire sentiment takes a 360-degree turn.
The ML-powered sentiment analysis offers an in-depth view of data as it has the ability to place individual phrases and words in different context-rich zones. It doesn’t only point out the black and white but can also distinguish between the various shades of grey-colored customer data.
