The Benefits Of Emotion Detection For Customer Experience Analytics

Emotion detection in text allows companies to discover new growth opportunities, enhance customer experience, explore product differentiation, reach new markets, do comprehensive quality checks, track emerging trends, and more. But the secret to truly leveraging emotion detection in customer feedback for CX analytics is to gain granular insights that may not seem to be in the data on a superficial level.

These fine-grained insights can only be extracted through machine learning-based text analysis tools that can identify positive, negative, and neutral emotions not just at an overall level but also at a more deep-rooted aspect level that is needed to truly understand customer behavior.

What is Emotion Detection?

Emotion detection is an automated process, powered by machine learning, that identifies and extracts sentiment from data. This data may be from text, audio, or video feeds from any kind of digital source.

There are four main ways in which an ML platform can detect emotion in data. These are as follow.

1. Detect emotion in text

An ML tool uses natural language processing (NLP) to analyze text and identify aspects and topics that seem to be occurring with a certain frequency. The tool identifies these as important elements in the text and ingests them for sentiment analysis.

Sentiment may be identified as love, hate, satisfaction, bored, grateful, or any other emotion based on the data, all of which can be categorized as positive, negative, or neutral emotion.

2. Detect emotion in audio

Audio feeds are transcribed so that they are converted into text and then this is analyzed for the sentiment expressed. Audio feeds could range from sources such as podcasts, sales calls, customer service calls, interviews, telehealth calls, or any other medium.

3. Detect emotion in video

Sentiment can be identified and analyzed in videos through machine learning algorithms that can capture text from caption overlays in the videos as well as the audio in it.

4. Detect emotion in social media chatter

Emotion detection can analyze audience sentiment in social media chatter, which can be more complex than linear data such as those derived from surveys or reviews. Social media conversations such as twitter feeds or social listening on Instagram, for example, may have to and fros, replies, arguments, hashtags, emoticons, code switches, and other details that can need advanced text analytics and sentiment analysis to detect emotion in text for positive, negative, and neutral feelings.

Why Do Companies Need Emotion Detection From Text?

Companies need to detect emotion in text because it sets the foundation for many critical business functions. These include:

1. Quality Control

Emotion detection helps companies analyze customer experience so that you can know what elements of your product and service need attention and improvement.

2. Build brand perception

Knowing market sentiment trends and purchase motivators help companies to develop more effective strategies to build brand perception and inspire brand awareness campaigns.

3. Sustainable growth

A company needs to have a sustainable growth strategy that is founded on grounded principles and not fads. Emotion detection can help companies in news sentiment analysis to track industry trends and market drivers so they can stay up-to-date and alert in a competitive market.

4. Understanding target audiences better

Emotion detection from review sentiment analysis from forums such as Reddit, Amazon Reviews, or Glassdoor, etc. helps a company categorize its types of customers and develop marketing strategies based on casual or repeat customers.

5. Find patterns in customer motivations

Customers may be motivated by various things and not just price alone. Emotion detection in text helps companies to figure out what the priority of these motivations are so that they can adjust them during marketing campaigns based on events, store locations, customer demographics, etc.

6. Market positioning

Sentiment mining helps companies in market positioning so that they know how to project themselves in order to create a niche market just for themselves. This helps them become competitive and resilient to shifting market dynamics to a great extent.

7. Finding new opportunities

Emotion mining can help companies go beyond their traditional scope of marketing and explore new markets. Text analytics can help them find TikTok influencers for a transformational digital marketing campaign, or explore new geographies that have expressed interest in similar products and services.

8. Personalized marketing

Personalized marketing is all the rage in ensuring that you elicit more customer engagement than traditional below-the-line or top-of-the-line marketing strategies. Emotion detection in text allows companies to extract insights from social media comments analysis, survey data analysis, or other sources of customer feedback in order to find cues that can lead to more targeted marketing tactics.

9. Effective advertising campaigns

Emotion detection gives companies a direction in which they should aim their advertising campaigns towards. The best way to reach audiences is to touch an emotional cord with them, and sentiment mining from customer feedback analysis can give you this insight readily.

10. Build customer loyalty

Emotion detection can help you build a loyalty program that will ensure that your customers support you even during times of turmoil as is the case with business transformations or during events such as the covid19 pandemic that are not in your control but can be disastrous for small companies.

How Can You Effectively Detect Emotion In Text?

In order to accurately and effectively detect emotion in text in an unbiased way you need to employ a text analytics and sentiment analysis solution. AI-driven text mining for emotion detection works in certain steps, which you can find below.

Step 1: Gathering data

Data for emotion detection can be gathered from various sources depending on your objective. These could be from text or video content from social media platforms for which you could use Live APIs. Or you could use data from surveys or other mediums which you can upload onto the sentiment analysis tool in an excel file.

Step 2: Text processing

All the cleaned content is now processed and ingested into the text pipeline. This includes data from all types including text, audio, and video. Among these, audio and video data are converted into text through text-to-speech software. Any caption overlays, emojis, or non-text data is then identified and extracted through natural language processing. This text is then segregated and categorized for topic classification, tags, and named entity recognition (NER).

Step 3: Emotion Detection

Now that all the text has been identified, they are categorized into aspects, themes, and topics for semantic analysis and emotion recognition. NLP tasks analyze grammar, sentence structure, tonality, adjectives, etc. to decipher recurring aspects. Semantic classification ensures that semantically similar text is grouped so you don’t get redundant emotion detection in text.

Finally, sentiment analysis calculates and assigns sentiment for aspect-based sentiment analysis and an overall sentiment score. This gives you a very fine-grained view of positive, negative, and neutral sentiment from all the crucial elements the text analytics API discovers in your data.

Step 4. Emotion Visualization

All the insights that have been extracted from emotion detection in text can now be cast on a visualization tool in an intelligent manner. This could be in the form of reports, numerical stats, word clouds, and more.

Read in detail how a Text Analytics API works

Conclusion

ML-based emotion detection in text gives you a more accurate picture of your data compared to manual analysis. This is especially important in brand management, social media marketing, or survey analysis at scale. Repustate’s text analytics and emotion detection tool gives you the ability to analyze data across languages, data sources, and data types with a precision that is higher than most tools in the market.

Based on whether you use a text analysis API or the full-fledged solution Repustate IQ that comes with an intelligent dashboard, you can even alter your aspects to customize your insights, or set alerts and notifications for specific keywords or hashtags to detect emotion.

In addition, you can get real-time sentiment analysis for immediate use or you could analyze data to detect emotion in text that is historic. You could also compare both past and current data for trend analysis, online reputation management, brand experience insights, and more.