As COVID-19 continues to accelerate the use and development of digital technologies in the market research space, we will continue to see changes in the way we collect and analyze data. One digital technology that has received a lot of attention recently is Emotion Text Analysis (ETA). Although ETA has been around for some time, advancements in machine learning and natural language processing have greatly impacted the evolution of this technology. In this article, we will discuss what ETA is, how it can be applied in market research and what are some of the latest innovations in this space.

What is Emotion Text Analysis?

According to Chew-Yean Yam, a Principal Data and Applied Scientist from Microsoft, “Sentiment Analysis aims to detect positive, neutral, or negative feelings from text, whereas Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise.” In other words, ETA goes beyond polar indicators to truly understand the moods and attitudes behind the overlying tone of the text. It seeks to discover the “why” behind the sentiment.

Applications of Emotion Text Analysis

As ETA has continued to expand over the years, so too has its applications in the real world. One of the most common uses of ETA is open-ended analysis in market research. This has helped researchers immensely as a task that would sometimes take an analyst days to complete can now be done in seconds with ETA. It also helps remove some of the subjectivity and variability created by human analysis.

In addition, companies like IBM have started using ETA in other capacities such as social listening, customer service and chatbot integration. In the pharmaceutical space, this can be used to understand the “why” behind patients’ actions and to develop a deeper comprehension of what it is like to live with a particular disease. This can help both doctors and pharmaceutical companies deliver better patient outcomes.

Latest Innovations in Emotion Text Analysis

With ongoing developments in the areas of Machine Learning and Natural Language Processing, ETA has immensely transformed over the years. One of those transformations is the speed in which we can now process data. As machines continue to get better at understanding and reading text, the speed at which they can collect, sort and analyze data has increased, allowing researchers and business personnel to respond to market changes faster.

Another innovation is ETA’s ability to now analyze data across multiple channels. No longer are researchers restricted to just survey response data. ETA software can now capture, sort and analyze data from surveys, customer satisfaction reviews, social media posts, published articles and more. Being able to analyze all these sources of data gives researchers a much larger scope.

Finally, ETA is starting to go beyond primary emotions like happiness, anger and surprise. Companies, like HeartbeatAI, are beginning to look at secondary emotions such as trust and boredom, adding greater depth and insight to traditional ETA.

Conclusion

ETA is one of many digital transformations we have seen over the years allowing researchers to gain deeper insights from their data at greater efficiencies. The advent of this innovation has allowed decision makers to respond faster to changes in the market and has given them a better understanding of the “why” behind overlying textual sentiments. It will be great to watch how this technology continues to advance in the years to come.