Emotion measurement is an ingrained skill that we nurture and enhance throughout our lives. In the digital age, we have sought to combine communication and technology. As a result, we can now measure emotions and communicate their meaning using AI and machine learning. In a broad sense, this advancement brings us closer to one another by bridging the communication gap caused by nuanced language in digital communication.
There are diverse and ever-changing ways in which people express their emotions in words. This is especially true for the elusive audiences who spend so much time in social and online conversations. This has been the driving factor in the quest to understand and analyze this un-natural language and drove us to develop our one-of-a-kind emotion measurement processing engine.
With this technology’s power, we can better understand each other and overcome the challenge of communicating without the support of physical and verbal cues. But how does this work? What tools are available to help us measure emotions when we are communicating with one another?
We’ve got everything you need to know about emotion measurement.
The Value of Emotion Measurement
Today, AI and machine learning combine to form an emotion measurement powerhouse. By addressing the limitations to traditional emotion measurement, platforms have been able to enhance the practice. Where closed-ended text responses were considered the gold standard, open-ended text responses are providing businesses with invaluable insights regarding respondents’ true feelings on a subject. Here are a few of the biggest changes:
AI-powered: The work of coding and evaluating open-ended text has shifted from humans to AI that can quickly do the work, making emotion measurement more accessible.
Nuanced Language Analysis: Those emoticons and internet slang phrases no longer require a massive effort to analyze. Not only does this encourage the increased use of open-ended questions, but it offers a broader range of applications, including social media sites.
Contextual Analysis: Evaluate text through the lens of the subject, not just the response alone. This upgrade makes emotion measurement more accurate than ever before.
These updates are a part of an emerging core component within the market research industry. With the ability to gain valuable insights from responders, businesses are likely to increase their reliance on the market research industry. In short, these updates and technological advances have secured the future of the market research industry.
Nuanced Emotion Measurement in Action
Nuanced emotion measurement empowers businesses to make informed decisions. With insights that go deeper than just assigning a positive, negative, or neutral value, companies can now understand what responders are actually trying to communicate.
This is not only the result of a shift towards open-ended questions, but it is also the result of real emotions being conveyed. The difference can be measured in these answers to the question: “Do you feel positive about our customer service?” And “How do you feel about our customer service?”
Closed-Ended Response: “No.”
Open-Ended Response: “No, I was annoyed that the lines were so long. I had a great experience otherwise.”
The closed-ended response gives the business no additional guidance or insight into why the respondent feels this way. However, the second response is more in-depth, describes a negative emotion, conveys the feeling of annoyance, and provides the business with actionable information.
With nuanced emotion measurement, businesses can take appropriate action, prioritize responses, and act with empathy. In short, nuanced emotion measurement adds context, so you can listen, not just hear.
The Application of Emotion Measurement
Emotion measurement, sometimes referred to as emotion analysis, is a metric used in market research to determine a responder’s feelings regarding a particular subject. Emotion measurement goes beyond traditional sentiment analysis.
The practice of sentiment analysis is assessing whether a response conveys positive, neutral, or negative meaning. Though sentiment analysis has many applications, it’s most commonly used to process natural language and analyze the text in market research.
For example, suppose a business sends out a survey to conduct market research. In that case, the business typically utilizes a mix of closed-ended and open-ended questions to gauge a respondent’s opinion on a given subject. The common question forms include a yes or no answer, a scale, or a numeric measurement, while open-ended questions require a respondent to answer using their own words and need more thoughtful answers than that of “yes or no” and “agree or disagree.” These questions offer qualitative data that can be quantified, whereas closed-ended questions are generally limited to collecting quantitative data.
The goal is to address the underlying issues that correspond to the negative and neutral assessments. In doing so, it gets to the bottom of the emotion, furthering the notion of why sentiment is merely a limited way of thinking in garnering this kind of data.
Limitations of Sentiment Analysis
Sentiment analysis has a host of limitations that are being addressed and remedied by advanced technology and methods. Here’s a list of the general limitations of sentiment analysis:
Lack of contextual analysis
Inability to effectively and efficiently evaluate open-ended text
Relies on positive, negative, and neutral assessments
Doesn’t accurately evaluate language in the digital age
Does not identify what a responder is trying to emote
Traditional sentiment analysis is simply too restricted, and in a sense, too distinct. Emotions are complex, and certainly don’t boil down to positive, neutral, or negative. Additionally, language has gained a new and complex layer with the introduction of “internet language.”
Today’s internet language features intricate nuances that blend and create new meaning from phrases that have already existed. Moreover, these phrases are made daily. Adding another layer of complexity is the frequent use of emoticons. Though their meaning is more easily discernible than some words, they still challenge traditional sentiment analysis when they are utilized in a nuanced manner.
For example, a Twitter user might respond to a tweet about a new comedy show trailer from Netflix. The tweet might read, “I’m crying!” and be bookended with two crying laughing emoticons. The meaning here is that the Twitter user feels positive about the trailer, making them laugh. This corresponds to a feeling of happiness. However, a traditional sentiment analysis platform might only decipher the phrase “I’m crying” and assign a negative assessment to the tweet.
Upgrade Emotion Measurement with Canvs API
Canvs utilizes three frameworks of emotion analysis options ranging from the most nuanced analysis stemming from their proprietary database of 42 distinct emotions to the less-nuanced emotion scale. Combining these frameworks with their semantic analysis technology, Canvs API can help you maximize your market research investment.
Canvs API is a simple and powerful tool that will change your expectations of emotion measurement forever. Discover the power of Canvs API.
