You might not think that emotion and artificial intelligence (AI) belong in the same sentence. However, a research field has emerged that uses AI to help marketers understand the emotional reactions that people have to the news as well as their experience with movies, products, restaurants, and other forms of consumerism. Emotional AI, also known as sentiment analysis, is a type of machine learning that analyzes negative, neutral, and positive views from a written report and uses it to gauge and understand customer reactions.

Sentiment analysis is useful to track psychological trends, analyze social media, and review the results of market research. Its software scans reviews, ratings, social media posts, and articles to pick up on the sentiments of the people completing them. This allows a marketers’ clients to form an aggregate from the ratings and use it to improve their service to customers.

Sentiment analysis uses technology that employs both linguistic algorithms and natural language processing to assign values to various customer responses. At the same time, machine learning accesses sets of data to uncover the most relevant trends that have occurred over a set time.

This requires a significant amount of planning. You must ensure that you use the right algorithms to capture the most useful information. It’s also important to analyze the proper phrases so you can convert your findings into improved experiences, products, and services. This technology makes it possible to identify the features that people prefer the most as well as those that tend to make them feel frustrated.

It’s Complicated to Gauge Emotions

different sentimentsHumans are complicated and fascinating beings, which means that sentiment analysis can be challenging to get right. People often don’t speak directly and hide criticisms into statements that appear positive on the surface. For example, someone could say that a product “wasn’t bad.” Does that mean it’s good or needs improvement?

Since this is such a new field, marketers are taking a variety of approaches in using it. At the same time, sentiment analysis is maturing. In the past, people have approached it using something called bag of words. This means that they create a list of all words used by customers as well as how often they used them. Such a method doesn’t consider the order of words at all. That means it would score someone rating a product as “not bad” as negative feedback.

As the technology has advanced, users now rely on neural networks that employ a technique called long short-term memory or LSTM. This enables them to condense an entire written sentence into a single vector. It takes the order of words into account when deciphering what a sentence means.

Businesses that serve customers directly find it too overwhelming to analyze all of their feedback manually. Using sentiment analysis as well as considering the context makes it possible to catch services issues and take corrective action as early as possible. The algorithms of machine learning can analyze large amounts of data and then quickly learn and perform certain tasks. The program sifts through the priorities that you previously determined to make it as accurate as possible. As this technology continues to expand, businesses that utilize it can expect to see a significant competitive advantage.