As with many A.I. tools, the first iterations of sentiment analysis promised so much, yet ultimately underwhelmed and failed to provide meaningful results. This natural : A.I. is only as good as the data it has been trained with, and improves over time. Google Translate spat out laughable results when first introduced, but now provides highly reliable outputs across many languages. We are seeing this process unfolding when it comes to sentiment analysis. We have experimented with Sentiment Analysis for many years, and have not been convinced until very recently. We believe that the time to start using it is now, and here is why.
Many preliminary tools were trained using a trivialized understanding of how language interacts with emotion. These tools lacked the ability to understand that different words could have drastically different meanings in opposing contexts, and thus often produced inaccurate results. In the search for big data to train tools, many turned to social media scraping, but this presented further language complications as the complexity and dynamism of online lexicon from meme culture and satire often resulted in the meaning of a passage becoming misconstrued.
In the past couple of years, we have seen a step change when it comes to the development and training of tools. Experts with decades of research and language training have developed tools with a powerful understanding of language and nuance, able to ascertain meaning behind complex passages and accurately depict sentiment. These tools have been trained using interview transcripts and conversations before being exposed to the online world, and online sources have been selected with far greater precision in order to cultivate an advanced learning process.
Early sentiment analysis focused solely on positive vs negative. While this may be helpful in determining how many people liked your brand or were happy with it in a given moment, its use in accessing the deep subconscious and predicting behavior was nonexistent. The full scale of human emotion is amazingly complex, and understanding this range has been a key to unlocking the potential for sentiment. Today we see tools not only identifying the 9 core emotions, but breaking these out into hundreds highly nuanced “micro-emotions”. The potential for this cannot be overstated – it is useful to understand that a person feels ‘sadness’, but understanding whether that sadness is experienced as ‘grief’ vs ‘empathy’ shockingly accurate attitude and behavior prediction.
Data is only as good as its application. While knowing which percentage of communications were positive or negative can give an idea as to overall sentiment, the true power of insight comes to life through through a far deeper examination. Comparing and contrasting nuanced emotion around key topics amongst individuals can inform with confidence as to who, how and why people are reacting to a brand, product, or situation, and give unprecedented power for innovation, communication, and forecasting.
If you want to find out more about how sentiment analysis can be used to accurately predict your customer behavior, get in touch today.