Sometimes it is seen that sentiment analysis can not help to its full extent in making the user understand what the customer actually feels. This defeats the very purpose of significant analysis and leads to an ineffective customer experience.
In such a scenario, emotion analysis comes into play. The process of identifying and analyzing the underlying emotions expressed in textual data is known as emotion analysis.
This can extract the text data from a number of sources to analyze the subjective information and gain an in-depth understanding of the emotions behind it. By leveraging emotion analysis, users can understand the emotions expressed by an author in a piece of text.
It can be easily executed on the basis of the feelings expressed in the text such as fear, anger, happiness, sadness, love, or inspiring. Large volumes of text data can be gathered and analyzed to know the emotions of followers or customers as explained in the article.
Let's dive deep to know why and how emotion analysis has significantly outperformed sentiment analysis.
Emotion analysis vs Sentiment analysis
Emotion Analysis and Sentiment Analysis both help to revolutionize the way people respond to new products. Content creators can enhance their brand reputation and customer experience by responding with custom-made offerings. This is made possible due to emotion and sentiment analysis.
Due to their overlapping advantages and functions, people tend to use emotion and sentiment analysis interchangeably. However, these systems have significant differences.
Though both the methods focus on figuring out a way to better understand the needs of the consumer, the difference lies in the way they approach this information. The words and emojis used in text in response to a particular ad or show can be analyzed by software. This information is used to understand and track people’s emotional fluctuations during ads.
This analysis provides a lot of insights in terms of age and genders like a particular ad, which emotions spike overnight, which brands are most popular as well as times, locations, and negative responses. A large number of companies and organizations are already using this innovative solution to work on their creative strategies.
(Suggested Blog: 7 NLP Techniques for Extracting Information)
Sentiment Analytics enables one to understand the general feelings and emotions experienced by a viewer. It doesn’t include particular emotions that are experienced. It just uses the contrast of positive and negative experiences to identify and analyze a broad response.
This helps in getting a general understanding of the success of the ad strategy or product along with knowing whether the product and customers get along with each other well or there is a scope of improvement.
This way it helps in gaining a better understanding of the public reception of a movie or TV series, new business, or the launch of a new product. It is an effective methodology to study and analyze affective states and associated information for learning more about the exact requirements of the customers. (Source)
Emotional Analysis uses a much more complex system for understanding consumer responses. While Sentiment statistics monitor simplified positive or negative markers, emotion analysis focuses on a broad spectrum of human emotions and sensitivities.
This method helps to measure the differences in the feelings that various viewers or buyers express either using emoji or textual data. Unlike sentiment analysis, emotional analysis takes into account the subtleties within human emotion.
The emotional analysis also includes the motives and impulses of a viewer. These helpful insights prove to be very beneficial for being translated into actions. A confused response can reveal that the designed content is too complicated to comprehend and you need to work on making more understandable and clearer content.
On the other hand, if the dominant feeling is boredom, you know that the need is to freshen things up with creative content or something that is engaging, informative, and interesting for the viewers.
(Recommended Blog: Emotional Artificial Intelligence)
How Emotion Analysis has Outperformed Sentiment Analysis
It reveals complex emotions
We all know that human emotions are too complex to be categorized as being simply positive or negative. If you are determined to strategize marketing and enhance your brand reputation, you definitely need a holistic view of your audience’s reactions.
While Sentiment Analytics categorizes opinion into negative, positive, or neutral, emotion analysis helps you to eventually meet the deeper needs of the consumer by letting you know the motivations and emotional blocks of your customers.
It provides deeply meaningful and relevant insights
Sentiment analysis provides you with basic responses, unlike in-depth Emotional Analytics that offers a comprehensive understanding of people’s actions and the motivations behind those actions.
As emotion analytics embraces the full spectrum of human emotions, it becomes much easier to get the right approach for tweaking content so that it can be of great success.
It helps to Turn Insight into action
By analyzing viewer responses more deeply than simply “positive” and “negative” you can tweak content to enhance the customer experience as well as the reputation of your brand.
In the plethora of online platforms emerging today, you can take the best course of action if you know the motivation behind each and every emotion being experienced.
These metrics can prove to be a big boosting aid in curating content that looks appealing as well as proves to be engaging to most of the customers.
(Related reading: Semantic analysis)
Different Methods and techniques for Emotion Analysis
There are a number of services available for tracking the thoughts and feelings of consumers. These can provide marketers with real-time data on consumers’ emotional triggers.
It helps in developing content that is not only the most pleasing but also has huge prospects of increasing the popularity and sales of the brand by boosting brand awareness.
When marketers exactly know how customers respond to content or products they are empowered to make predictions about how new content should be structured, which aspects to strictly avoid, and which to boost.
While some content may need just a little tweaking, others should be stopped completely in case they might evoke a negative reaction. This helps to avoid expensive campaigns that are of little or no value.
(Also check: Sentiment Analysis of YouTube Comments)
While ending the blog we can conclude that the future of effective marketing strategy lies in tracking public emotion to the greatest extent possible.
For instance, if you ever walk down to Birmingham’s New Street Train Station, you will be able to see digital screens placed there by Ocean Outdoor so that they can track the age and sex of people that walk by in order to show more relevant advertising on nearby billboards.
This is just a small example to show the potential of Emotion Analysis to be applied in millions of organizations in hugely different and creative ways. It would not be wrong to say that emotion analysis is bound to lead the way to a much well-strategized future of digital marketing.