Humans are complex. In research, participants often find themselves limited to numbers and words to express themselves. Emotion AI goes beyond traditional research processes, interpreting human emotions for a more in-depth understanding of the topic at hand. This relatively new technology is taking the market research industry by storm, allowing MRX professionals to capture and analyze emotions for more informed decision-making. Here is a glimpse into the three common uses of emotion AI and the benefits of this groundbreaking technology.
Extracting emotional insights from text requires sentiment analysis- a process that uses natural language processing to identify and quantify positive/negative emotions from text samples. Natural language processing (NLP) harnesses machine learning algorithms and techniques to help researchers determine intent and categorize users. Sentiment analysis uses three main methods; rule-based, statistical, and hybrid. Rule-based sentiment analysis determines the emotions in a sentence by classifying text based on affected words. These words often correlate directly with a specific sentiment such as happy, sad, angry, etc. When using the statistical method, researchers uncover emotions using an ML model. This model helps identify the product and holder in a complex sentence. Finally, hybrid model sentiment analysis combines rule-based and statistical methods to detect subtly expressed sentiments.
Applications of this process include analyzing customer reviews, surveys, social media content, and other online materials. Using sentiment analysis tools can help businesses analyze KPIs and customer satisfaction levels, compounding and analyzing large amounts of online data to provide insight into overall brand sentiment.
Sifting through online data remains time-consuming and may not account for every piece of information on the web. Using AI technology saves researchers time finding and analyzing relevant consumer insights, helping unmask consumer sentiments and create data-driven decisions in less time.
Improve Social Listening
With a large amount of information available on the web, keeping track of consumer insights pertaining to a specific brand can be difficult. Utilizing AI technology simplifies this process, allowing researchers to more easily attain and analyze customer feedback to improve overall experiences and brand strategies.
Become More Agile
Staying up to date on customer behaviors and sentiments can help businesses identify trends and adjust quickly to changing preferences to stay ahead of competitors and provide exceptional customer experiences.
Researchers can utilize AI technology through video platforms to monitor human emotions in relation to various stimuli. Facial movement analysis remains a frequently used measuring tool for identifying facial micro-expressions to uncover consumer sentiment. These tools work by identifying key features of the face to analyze changes in these features, attributing facial expressions to emotions. Through deep learning, AI video technology can recognize universal emotions, helping researchers go beyond traditional qualitative and quantitative practices. With this, researchers can analyze consumer engagement, emotional activation, and the overall impact of specific topics. Video AI technology uncovers insights in advertising campaigns alongside user experience, brand, concept, and product testing. Researchers can utilize this method in qualitative group settings such as focus groups or individual settings using personal computers and smartphones.
Unlike other forms of emotion AI, video analysis allows researchers to track eye movements, identifying areas of focus to gauge which elements draw in research participants. This becomes very useful when testing advertising campaigns, as this data provides insights into the effectiveness of specific design elements.
As mentioned above, researchers can use video to measure emotions in both group and individual studies. With this, video can be used alongside methods, such as audio, to capture emotions from both verbal and non-verbal indicators.
Researchers have begun using AI technology to decipher emotion in audio. Speech emotion recognition (SER) processes and classifies speech signals to detect and analyze the sentiments of a speaker. These programs go beyond words spoken, analyzing vocal tone to determine emotions.
Add More Meaning to Words
When conducting qualitative studies, emotion AI technology helps researchers dive deeper into the responses of participants. Analyzing emotions through vocal tones concerning specific topics, words, and phrases provides a better understanding of consumer intent and emotion for more informed and impactful decisions.
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