Transforming YouTube Comments into Emotional Data with AI Emotion Recognition

YouTube comments are a goldmine of raw, unfiltered emotional reactions from viewers around the world. While likes and dislikes offer surface-level feedback, online comments carry emotional weight that goes much deeper. Text-based emotion analysis helps uncover this depth, revealing the true emotion behind the words. At Imentiv AI, our Text Emotion Recognition API offers a much deeper understanding of user reactions through comment emotion analysis.

What Is Text Emotion Analysis?

Text emotion analysis is the process of using AI emotion recognition tools to detect and interpret emotional cues embedded in text. These systems analyze word choices, syntax, context, and even emojis used to label emotions such as sadness, joy, anger, fear, nostalgia, and more. When applied to user-generated content like YouTube comments, it reveals how audiences feel rather than just what they say.

Instead of just labeling a comment as "positive" or "negative," this approach delves into the specific emotions being expressed within the context of the conversation. 

Think about a YouTube comment expressing "surprise", is it a pleasant surprise about a plot twist, or a negative surprise about a technical glitch? Understanding this nuance is crucial for content creators, brands, and anyone seeking genuine insights from online interactions.

Why Use Emotion Detection from Text?

Using emotion detection from text allows creators, marketers, researchers, and content strategists to:

• Measure audience emotional engagement at scale
• Identify which parts of the content evoke strong emotional responses
• Understand viewer reactions without needing surveys or interviews
• Spot emotional trends across geographies, languages, and time
• Tailor future content to resonate emotionally with specific audiences

With AI emotion recognition online tools, you don’t need to manually interpret thousands of comments, you can extract actionable emotional data in seconds.

As an AI-driven text emotion recognition platform, Imentiv AI goes beyond basic sentiment analysis. Our text emotion detection tool analyzes comments sentence by sentence, identifying the presence and intensity of not just a few broad emotions, but a spectrum of 28 distinct emotional categories. From subtle "caring" to outright "anger," from hopeful "optimism" to genuine "gratitude," Imentiv AI paints a detailed picture of the emotional undercurrents in any text.

How Does Text Emotion AI Work?

AI-based text emotion tools (like Imentiv AI) operate in three layers:

1. Preprocessing: It removes noise, interprets emojis, and cleans the language.
2. Emotion Detection Model: Using natural language processing (NLP) and deep learning, it maps words and phrases to emotion categories such as joy, sadness, trust, fear, disgust, anticipation and more.
3. Aggregation & Visualization: It consolidates emotional signals across all input text and presents them in digestible formats like charts, graphs, or summaries.

This helps even short-form user text, like YouTube comments, become powerful emotional indicators.

In this blog post, we’ll explore how our Text Emotion Recognition tool can help decode the emotional tone behind these comments. For this analysis, we focused on "See You Again" by Wiz Khalifa featuring Charlie Puth, a song that strikes a deep chord with listeners, evoking themes of loss and remembrance.

Real-World Use Case: Emotional Insights from "See You Again" YouTube Comments

Our AI-powered emotion recognition tool analyzed approximately one hundred comments from the selected YouTube video. This analysis focused on identifying the dominant emotional tones expressed by viewers, both on an aggregate level and on a sentence-by-sentence basis.

Aggregate Emotion Overview

Our text emotion analysis tool identified curiosity as the dominant emotion across the entire comment set, accounting for 29.12% of the emotional tone. This suggests that the video sparked significant interest and questions among viewers.

Sadness was the second most prominent emotion detected, contributing 15.33% to the emotional landscape. This indicates that several viewers connected with the video on an emotional level, possibly due to its content or themes.

The third most notable emotion was love, present in 12.84% of the comments, reflecting a strong emotional attachment or appreciation from the audience.

Other emotions detected in the comment set include:

• Admiration
• Joy
• Caring
• Excitement
• Neutrality
• Grief
• Confusion
• Optimism
• Disappointment
• Surprise
• Desire
• Fear
• And more.

This variety highlights the emotional richness and complexity of audience responses to the video.

Sentence-Level Emotion Analysis

The sentence-level breakdown revealed a sequence of emotions including:

• Admiration
• Neutral
• Excitement
• Sadness
• Joy
• Caring
• Love
• Joy (repeated)
• Admiration (repeated)
• And more.

This progression demonstrates subtle emotional shifts and indicates that viewers moved between states of appreciation, emotional resonance, and excitement.

Interactive AI Insights

Within the insights tab of our Text Emotion Recognition tool, users can explore deeper emotional trends in the text using guided questions such as:

By selecting any of these questions, users receive real-time emotional insights generated from the analyzed data, offering a more intuitive understanding of the emotional patterns.

Downloadable Results

Users can download the complete emotion analysis results in CSV format, allowing for further analysis, reporting, or visualization as needed.

Check out this blog to understand how Imentiv AI captures emotions from every format.

Now let’s look at the breakdown of the dominant emotional themes detected in the text:

Viewers Express Deep Sadness and Grief

Our tool detected a high concentration of emotionally intense words and emojis indicating grief and pain. Many viewers use language associated with mourning and openly express emotional distress. Phrases that convey “it still hurts,” “I’m crying,” or “I miss him” appear frequently. The recurring use of crying and broken heart emojis further amplifies this emotional tone. This indicates that, for many, the video remains a space for public grieving, especially in relation to Paul Walker.

Nostalgia Dominates the Emotional Tone

The analysis finds nostalgia as a recurring emotion throughout the dataset. Viewers often describe how the song takes them back to earlier times, particularly memories tied to the Fast & Furious film series or their own life events. Phrases like “this brings back memories” and “feels like yesterday” appeared repeatedly. This shows that the song doesn’t just evoke sadness, it also helps people reconnect with personal and shared histories.

Audiences Show Strong Emotional Appreciation for the Song

Our system identified numerous expressions of positive emotional impact linked directly to the song’s musical and lyrical quality. Many users praise the composition, vocal performance, and emotional delivery. Words such as “masterpiece,” “beautiful,” “timeless,” and “perfect song” frequently emerged. These expressions highlight how users emotionally engage with the artistry of the music itself, independent of its context.

Comments Overflow with Love and Affection for Paul Walker

The tool recognized language patterns showing affection, love, and admiration, particularly directed at Paul Walker. Viewers often express how much they still care for and miss him, using phrases such as “we love you” and “forever in our hearts.” These messages reinforce the emotional attachment people still hold, not only to the song but to the person it honors.

The Tool Captures the Song’s Lasting Emotional Impact

Many users reflect on the fact that the song continues to affect them, even years after its release. There were time-referenced comments like “It’s 2025 and this still hits,” or “never gets old.” These remarks signal a sustained emotional resonance, and our tool was able to identify them as key indicators of the song’s enduring legacy.

Audiences Share a Collective Emotional Experience

The comments show a communal response. There were multiple instances where users referenced others in the comment section, asked who else was watching in the current year, or shared gratitude for the shared emotion. This points to a bond formed through collective grief and memory, and showcases how our tool can recognize patterns of shared sentiment across user interactions.

Many Comments Reflect Personal Stories of Loss

In several comments, viewers relate the song to their own experiences of personal grief, unrelated to the original context of Paul Walker. The tool identified emotionally heavy phrases like “this reminds me of my friend who passed away” or “I lost someone recently.” These instances show how the song functions as an emotional outlet for those who are processing their own losses, and how our tool can detect these personal emotional extensions.

Listeners Report Physical Reactions to the Song

Our tool also picked up on phrases that describe physical manifestations of emotion, such as “goosebumps,” “tears,” and “chills.” These reactions appeared consistently in the comments, especially when viewers mentioned specific timestamps in the song. By detecting such responses, our emotion recognition system reveals how emotional intensity translates into sensory experience, a key metric in understanding deep engagement.

Viewers Acknowledge Paul Walker’s Lasting Legacy

Finally, many commenters use emotionally rich language to honor Paul Walker’s legacy. The tool identified expressions of gratitude, tribute, and reflection, such as “you made us smile,” or “you’ll never be forgotten.” These statements contribute to the tone of the comment section as a living tribute, and demonstrate how the emotion tool captures respect, memory, and legacy as emotionally coded signals.

This case study highlights how our Text Emotion Recognition technology transforms unstructured emotional data into clear insights that creators, marketers, and researchers can use to understand audience impact at scale.

To see the comment analysis results or experience Imentiv’s Text Emotion AI in action, click here.

Our text emotion API empowers you to seamlessly integrate this advanced emotion analysis into your existing workflows and applications. 

Read our blog to learn how developers are integrating emotional intelligence into their apps.

Imagine the possibilities: content creators gaining granular feedback on specific segments of their videos, marketers understanding the emotional impact of their campaigns, or customer support teams identifying and prioritizing urgent, emotionally charged inquiries. 

Furthermore, Imentiv AI's capability to analyze bulk data delivers instant, scalable insights, allowing you to process thousands, even millions, of comments with remarkable speed and accuracy. By providing this nuanced, context-aware understanding of emotions, Imentiv AI transforms raw text data into actionable intelligence, unlocking deeper connections with your audience and enabling more informed decision-making across a multitude of scenarios.

Need Custom Emotion Intelligence for Your Business? Contact our sales team to discuss tailored solutions for your text or media analysis needs.

Visit imentiv.ai for all-in-one solutions across text, video, audio, and image emotion analysis.