Inspired by the configuration of the brain, sentiment analysis algorithms imitate how the human brain processes data through an artificial network of neurons. NLP Deep Learning uses powerful neural network algorithms to carry out sentiment analysis. This is an emergent technology used to detect if a chunk of text is positive, negative or neutral. It does this by assigning sentiment scores to categories, topics or entities. These categories could be specific stores, products, pricing strategy, locations, promotions etc.
It is used widely to gauge customer opinions online. A new use case is where companies are having to deal with the fallout of high attrition rates. HR teams are using data analytics conjoined with sentiment analysis to understand what employees are talking about to reduce turnover and improve performance. This is not personalized as in pinpointing a particular person but understanding general trends and take corrective measures if needed.
The importance of this technology is proven. If you are considering integrating this into your analytics system. It’s good to understand what is involved in setting it up. This article gives you an overview of a deeply technical process.
It all starts with building a sentiment library
Sentiment libraries are made of multiple dictionaries that have an exhaustive list of phrases and adj