NCC April Sentiment Like a Humanoid
April 27, 2022
Artificial intelligence algorithms are dumb when it coms to interpreting human emotions. Human emotions are extraordinary complex, especially when rendered in text or emojis. There is a goldmine of information for organizations to use to their advantage if only sentiment analysis could be perfected. Brandwatch is working on sentiment analysis perfection and discuss their latest endeavors in the blog post: “Interview: The Data Science Behind Brandwatch’s New Sentiment Analysis.”
Brandwatch recently deployed a new sentiment AI model to over one hundred million sources covered in Brandwatch Consumer Research and apps the company powers. The upgrade provides 18% better language accuracy, it is also multilingual, and add sentiment analysis to all languages. Sentiment analysis is a key component Brandwatch offers its customers, because it aids in assessing brand health, detects potential circuses, identifies advocates/detractors, and discovers positive and negatives topics associated with the brand.
Colin Sullivan is a Data Science Manager, who heads different Brandwatch projects involving linguistics and computational linguistics. Sullivan explained that Brandwatch wanted to implement a new way of analyzing sentiment, because the company wanted to use new state-of-the-art developments and simplify the process.
The new model uses transfer learning, which is how a human brain works. The model gains a general understanding of a task, then transfers its newly knowledge to a new task. It is an improved model because:
“One of the key advantages of this new approach is that it makes it more robust when dealing with more complex or nuanced language. The new model can see past things like misspellings or slang. Previously, supervised learning models would be restricted to a fixed set of known patterns during training, which did not come close to exhaustively capturing all linguistically plausible ways of expressing a concept. New state-of-the-art models are better able to re-use what it already knows when faced with new or rare patterns. The transfer learning approach means the model will take what it knows to fill in gaps…And it works in almost any language because we are not training for a new language each time. This also means it can handle a wider range of regional dialects and posts where someone switches between languages.”
The new model has a 60-75% accuracy rate of the sentiment in content. If that fact holds up, AI could soon understand sarcasm. It would be helpful if they could also detect fake reviews from Karens/Kyles or bots.
Whitney Grace, April 27, 2022
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