Sentiment Analysis vs Emotion Detection: what is the difference?
The amount of unstructured data generated by organizations, communities, companies, and products continues to grow exponentially. A huge amount of unstructured data is textual communication between companies and customers via reviews, open-ended questionnaires, support tickets, etc. This information is like a rough diamond, it may contain various insights which can help businesses evaluate and enhance customer experience. And when there is a need to analyze text, natural language processing comes into play.
Natural Language Processing (NLP) gives machines the ability to break down and understand human language. Two of the top-used NLP techniques are Sentiment and Emotions analysis. Both deal with detecting emotions yet there’s quite a difference between the two. In this article, we will explore the differences between sentiment and emotional text analysis to help you decide which approach is right for your needs.
What is sentiment analysis?
Sentiment analysis, or opinion mining, is the most popular text classification task. With a combination of natural language processing and machine learning algorithms, it can automatically extract subjective information and identify it as positive, negative, or neutral.
Depending on the set of tasks, it is possible to adjust the sentiment analysis models so that they can go beyond the polarity and detect specific feelings and even intentions. Here are the most relevant types of sentiment analysis:
- Graded sentiment analysis
- Aspect-based sentiment analysis
- Intent analysis
- Emotion detection
As we can see emotion detection is one of the types of sentiment analysis. Sentiment analysis has a variety of uses including analyzing customer feedback, tracking brand reputations, or evaluating public opinion on a topic. But in some cases, it might not be enough to understand what the customer really feels. Sarcasm or irony, for example, can be a true challenge for sentiment analytic tools. That’s when emotion analysis is truly needed.
What is Emotion Analysis?
Emotion Analysis, emotion detection, or emotion recognition uses more advanced machine learning techniques to analyze more complex emotions like fear, anger, sadness, love, frustration, and many more.
For example, if we compare two statements: “I don’t like the interface” and “I hate this product” they are both negative. But it’s obvious that there’s a huge emotional difference between “don’t like” and “hate”. This method helps to measure these subtleties within an emotion, and even analyze the motives and impulses of customers.
There are also limitations and downsides to emotion detection. Even with all the experience humans have, it still can be tricky to understand someone’s emotional state. And humans label those datasets machines use for emotion detection, based on their subjective experience which might not always be relevant to the situation.
Emotion or Sentiment, which is better?
Both approaches work great providing valuable suggestions to understand and interpret the audience’s feedback. Choosing sentiment analysis or emotional detection one should rely on a specific task, whether a general understanding of negative or positive customers’ attitude is enough or the goal is to enchance the whole brand reputation.
I made up three average reviews with some tricky moments:
• I hate this product. - this one is obviously for Emotion Detection, as it’s not just negative, it shows an emotion - anger.
• I don’t like the interface. - Sentiment analysis is enough here to understand that it’s a negative statement.
• I love this brand to death! - This one is tricky, as there’s a slang phrase “love to death” which is positive and shows happiness.
• This is cool, I regret not trying it earlier. - Despite the word “regret” this one is positive as well.
See the picture below of how it looks in the studio:
As seen from the example emotional recognition reveals more complex emotions thus can provide with meaningful and more relevant insights. By analizing reviews more deeply than just “positive” or “negative” we can not only improve customer service but also make predictions about how the upcoming product is met by the customers.
Of course, it worth considering that deeper emotional analysis involves more resources and investments, while the sentiment analysis does it’s job and doesn’t require much effort. Eventually whatever the goal is each business has it’s own understanding of when emotional analysis is beneficial and when it is more practical and effective to perform a sentiment analysis that is less in-depth. Ultimately, the combination of the two systems can be successfully used to help driving improvements in customer service and retention.
Sentiment and emotional analytics are often used interchangeably in terms of tracking consumer behaviors and sentiments. Still, emotional analysis is considered to be a more sophisticated solution that digs deeper into specific emotions and helps turn insights into action. Though, along with the exciting opportunities more complex analytics requires time and recourses.
However, with the developments of the ready SaaS solutions, sentiment and emotional analysis is available for people with no coding skills and machine learning knowledge. With One AI you can use both features based on pretrained models, so you don’t need any preparation either. Check out our Language Studio and use a powerful mixture of analytics for your needs.