Why Emotion Recognition API in Transcript Analysis for Sales, Therapy & More (Part-1)
Transcripts are more than just records of conversation; they're often the only accessible format left behind after important exchanges. Whether drawn from audio, interviews, or recorded calls, these texts capture not just what was said but hint at how it was told. To understand these transcripts, especially in sensitive or high-impact contexts, we need to grasp the emotional undertone within the language itself. This is where Text Emotion Analysis becomes essential. Powered by Emotion Recognition technology, our Text AI (API) system analyzes linguistic patterns across 28+ distinct emotion categories. Based on this layered analysis, it identifies overarching emotional states such as anxiety, enthusiasm, doubt, confidence, optimism, and more, transforming plain transcripts into emotionally enriched insights.
Revisiting a transcript isn’t about recalling what was said; it’s about re-evaluating how it was told. That act of re-reading can reveal emotional layers that influence decisions, relationships, and outcomes. This is where our text-based emotion analysis adds real value: by detecting emotional context hidden in seemingly neutral words.
Organizations and professionals use transcript emotion analysis in a variety of real-world scenarios where emotional understanding drives better outcomes.
A recent study (https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1504306/full) used emotion recognition AI to analyze psychotherapy session transcripts and found that specific emotions could help predict patient symptom severity and the strength of the therapist-patient relationship.
For example, emotions like anger and fear were linked to more severe symptoms, while curiosity and surprise signaled stronger emotional connection and trust.
This shows how emotion analysis from text can uncover deep insights into mental health and therapeutic outcomes, offering a powerful use case for AI in real-world conversations.
From therapy sessions and customer service calls to user interviews, sales conversations, courtroom dialogues, and coaching sessions, analyzing the emotional tone in transcripts can uncover underlying emotional dynamics, guide better decision-making, and strengthen interpersonal understanding.
What is Imentiv Text Emotion Detection?
Imentiv’s text-based emotion detection technology goes beyond simple sentiment analysis. Instead of just tagging text as positive or negative, our system performs sentence-by-sentence emotion analysis from text, identifying over 28 distinct emotions with precision.
If you're wondering how to detect emotions in text , our Text Emotion API makes it simple. It takes any transcript or written content and uses advanced language models to run text-to-emotion conversion, spotting emotional signals that often go unnoticed.
Our AI model can detect a wide range of emotions, such as: joy, love, pride, admiration, excitement, gratitude, relief, caring, amusement, surprise, optimism, relaxed, curiosity, as well as emotions like anger, fear, nervousness, grief, sadness, confusion, disappointment, disgust, annoyance, remorse, embarrassment, desire, disapproval, realization, and approval.
This detailed text emotion detection helps users understand how people feel during conversations, feedback, interviews, therapy sessions, or any text-heavy interaction.
Real-World Scenarios: How Imentiv Text Emotion AI Analyzes Different Contexts
Let’s take a look at some real-world scenarios where transcripts carry rich emotional context. Using Imentiv’s text emotion AI , we’ll analyze how emotions unfold in each case, what patterns emerge, and how those insights can be used for better understanding and decision-making.
SaaS Sales Call Emotional Flow Breakdown
A sales representative introduces a SaaS productivity platform to a potential client experiencing challenges with remote work and missed deadlines. The conversation focuses on the product’s key features, real-time dashboards, async updates, and sentiment tracking, as well as ease of onboarding and integration with existing tools like Trello and Slack. The client asks pointed questions, indicating interest and evaluation of fit.
👉 See how Imentiv Text Emotion AI breaks down the emotional flow of this sales call. (Click to explore the dashboard )
The overall emotional tone of this sales exchange is anchored in optimism , suggesting that while the client expresses initial concerns, the conversation steadily moves toward a hopeful and constructive trajectory.
Emotion insights at key stages:
- Opening (Curiosity + Empathy)
Rep: “Thanks for joining today, Arun. I heard remote work has been stressful for your team.”
The opening sets an empathetic tone. Curiosity is paired with emotional sensitivity, which establishes trust and signals that the rep is tuned in to the client's context.
- Client response (Disappointment)
Client: “Very. Everyone’s overworked, and we keep missing deadlines.”
This statement carries clear disappointment , revealing the emotional strain and dysfunction the client is experiencing. It's a moment of vulnerability that indicates unmet needs.
- Solution introduction (Curiosity + Tentative Interest)
Client: “What exactly do you offer?”
The client’s question signals continued curiosity . It’s a pivotal moment that marks a transition from problem disclosure to solution exploration.
- Product explanation (Curiosity intensifies)
Rep: “Real-time dashboards, async updates, and team sentiment tracking.”
This concise breakdown sustains engagement. Emotion analysis shows increased interest, which is when the client begins to consider alignment between their problem and the offered solution.
- Learning curve (Admiration emerges)
Client: “What’s the learning curve like?”
Rep: “It’s minimal, onboarding takes under 30 minutes.”
The client's response, “That’s impressive” , marks a clear emotional shift to admiration . Ease of use often resonates strongly in a B2B context, where frictionless onboarding is highly valued.
- Feature fit (Approval builds)
Client: “Can it integrate with Trello and Slack?”
Rep: “Absolutely. Seamless integrations with both.”
The rep’s confident assurance leads to rising approval . The solution now aligns with the client's existing tools, reducing friction and increasing perceived value.
- Closing sentiment (Cautious Optimism)
Client: “Hmm. I’m cautiously optimistic.”
This line encapsulates the dominant emotional arc. There’s a subtle mix of hopefulness and reservation , a sign of growing trust that still leaves space for follow-up and deeper validation.
Through Imentiv AI’s emotion recognition from text, this sales pitch reveals more than just interest; it captures a narrative of emotional conversion . From early disappointment to mid-stage admiration and eventual approval , the analysis shows how emotional shifts occur in tandem with the rep’s messaging.
This kind of analysis is especially powerful in sales enablement, helping teams understand what types of phrases or pain-point acknowledgments generate emotional resonance. While the dialogue may sound smooth on the surface, emotion detection pinpoints where engagement strengthens , when objections soften , and how optimism builds , all critical markers of a successful pitch flow.
Startup CEO Interview: Emotional Journey Insights
In this one-on-one interview, a startup CEO reflects on the emotional and logistical hurdles faced during early fundraising and company growth . The conversation highlights moments of intense pressure, such as multiple funding rejections, personal sacrifices, and the emotional toll of leadership. The interviewer guides the CEO through personal memories and professional turning points, creating a narrative around resilience, vulnerability, and team support .
👉 See how Imentiv Text Emotion AI captures the layered communication in this raw and reflective CEO interview. (Click to explore the dashboard )
Using our text emotion AI , the dominant emotion detected throughout the interview is realization , a reflective state that emerges as the CEO revisits defining hardships, emotional lows, and personal regrets. This emotional theme gives the interview gravity and vulnerability, moving it beyond surface-level success to reveal authentic human experience.
Line-by-line emotional reasoning:
- Interviewer : “People see your success, but what moment nearly broke you?”
Curiosity is present here. It’s a question that goes beyond metrics, inviting vulnerability and emotional truth.
CEO : “Series A was brutal. We got 28 rejections before one yes.”The dominant emotion is disappointment . The CEO doesn’t just describe a business setback; it’s a moment of repeated rejection that deeply affected confidence and momentum.
- Interviewer : “How did that feel emotionally?”
Curiosity continues. The interviewer is intentionally creating space for emotional processing, not just factual recounting.
- CEO : “Like I’d let the whole team down. I cried the night before payroll.”
This is a moment of sadness and vulnerability . The CEO admits to emotional collapse, a raw disclosure that reveals the immense pressure founders carry. Crying before payroll suggests fear, shame, and the weight of responsibility.
- Interviewer : “You still showed up for them.”
This line acknowledges resilience and introduces approval . It’s a subtle affirmation, spotlighting the CEO’s strength despite emotional hardship.
- CEO : “They showed up for me. That’s when I knew I had the right people.”
Here, gratitude and approval are both present. The shift is powerful, the CEO recognizes not just the burden of leadership but the strength of mutual trust. This marks a turning point from despair to support.
- Interviewer : “And what about personally?”
Another moment of curiosity , now inviting emotional reflection on the CEO’s private life, adding complexity to the narrative.
- CEO : “My daughter was born during launch week. I missed her first smile.”
Sadness dominates here. This isn’t just about missing a milestone; it’s a symbolic moment of personal sacrifice. The emotional cost of ambition surfaces strongly.
- Interviewer : “That’s tough. Any regrets?”
A gentle nudge for deeper introspection, still carrying curiosity but laced with empathy.
- CEO : “Only that I didn’t ask for help sooner. I thought I had to be invincible.”
This line anchors the entire emotional arc in realization . It reflects self-awareness, remorse, and emotional growth. The CEO recognizes that strength isn’t invincibility, it’s the ability to lean on others.
- Interviewer : “That’s incredibly human of you to share.”
The conversation ends with admiration . The interviewer acknowledges the CEO’s honesty, modeling a tone of deep respect for emotional transparency.
Imentiv Emotion AI effectively captures not just what was said, but why it resonates. The conversation follows an emotional arc, from disappointment and sadness to gratitude, and finally to realization. By decoding these shifts, your tool reveals the internal landscape of a founder’s journey, something traditional sentiment analysis often overlooks.
Therapy Session: Emotional Layers Explored Deeply
This is a therapy dialogue centered around a client’s ongoing struggle with burnout, internalized guilt, and deep-rooted beliefs about self-worth and rest. The therapist gently guides the client through unpacking inherited mindsets and exploring what rest might look like without judgment. The session reveals a tension between exhaustion and emotional permission, as the client seeks validation beyond productivity.
Explore how Imentiv Text Emotion AI interprets layered thought patterns in this reflective therapy session. (Click to explore the dashboard )
Using our text emotion AI, the dominant emotion across the transcript is admiration . This doesn’t stem from a single statement, but rather builds over time through the therapist’s validating tone and the client’s courageous vulnerability. Together, they form a dialogue rooted in emotional honesty and mutual respect.
Sentence-by-sentence interpretation with emotional reasoning:
- Therapist: “You mentioned burnout last time. How are you feeling now?”
Curiosity is detected. This is an open, attentive question that invites the client to reflect inward without pressure. The therapist is gently returning to a previously explored theme.
- Client : “Still tired. But now I feel guilty for being tired.”
Remorse is the dominant emotion here. The client isn’t just physically exhausted; they’re emotionally burdened by guilt, which points to internalized shame around rest.
- Therapist : “That guilt, where do you think it comes from?”
Again, curiosity is present. This question invites deeper self-exploration, without judgment. It encourages the client to connect emotions to learned patterns or past conditioning.
- Client : “I was raised to believe rest equals laziness.”
This marks a moment of realization . The client is verbalizing a core belief, likely internalized early in life, that now contributes to emotional conflict.
- Therapist : “That’s a heavy mindset to carry.”
This is a neutral yet empathetic observation. The therapist isn’t reacting with emotion but is mirroring the client’s burden, validating its weight.
- Client : “I know, but I can’t switch it off.”
Here, approval is detected. The client affirms the therapist’s comment but also acknowledges the challenge of unlearning this belief. The tone shows both agreement and frustration.
- Therapist : “What would happen if you let yourself rest guilt-free?”
Curiosity returns. This is an open-ended, imaginative prompt, encouraging the client to envision a reality without guilt, perhaps for the first time.
- Client : “I don’t know. Maybe I’d feel… free? Or anxious.”
This line reveals confusion . The client feels torn between a longing for freedom and a fear of change. The hesitation and uncertain tone highlight an unresolved emotional state.
- Therapist : “It’s okay to hold both feelings.”
This is where approval surfaces again. The therapist normalizes ambivalence, showing that complexity is valid and safe. This kind of affirmation fosters emotional trust.
- Client : “I just want to feel like I’m enough without overworking.”
Desire is the dominant emotion here. The statement is raw and emotionally direct. It shows the client’s yearning for self-worth not based on output, but on being.
- Therapist : “That’s a beautiful goal, and we’ll work toward it together.”
Admiration peaks here. The therapist is honoring the client’s vulnerability, naming their emotional goal as something deeply meaningful and worthy of support.
This conversation showcases how our Text Emotion AI can trace an emotional arc, from remorse and realization, through confusion, toward desire and ultimately mutual admiration. The ability to extract such nuance helps therapists, coaches, and researchers better understand not just what is said, but what is felt.
Job Interview Candidate’s Emotional Growth Story
In this job interview setting, the candidate reflects on a past professional failure tied to communication issues, illustrating how that experience led to personal and team-oriented growth. Through a series of open-ended questions, the interviewer uncovers the candidate’s journey from self-blame to self-awareness, emphasizing lessons in leadership, vulnerability, and emotional maturity, qualities aligned with the company’s culture of empathy.
See how Imentiv Text Emotion AI captures reflective growth and professional values in this job interview exchange. ( Click to explore the dashboard )
Using our text emotion recognition AI, the dominant emotion detected across the full transcript is disappointment. This is driven by the candidate's reflective tone when recounting a professional failure that deeply impacted their sense of self and leadership. The emotional weight of this memory sets the overall tone of the conversation.
Sentence-by-sentence emotional interpretation:
- Interviewer : “Tell me about a failure that changed how you work.”
→ Curiosity is detected here, as the question is open-ended and invites the candidate to introspect and reveal something meaningful. The tone is probing, but non-judgmental.
- Candidate : “I once led a project that collapsed due to poor communication.”
- Interviewer : “That must’ve been difficult.”
- Candidate : “It was. I blamed myself for months.”
- Interviewer : “What did you learn from it?”
- Candidate : “To overcommunicate, to ask questions, even when it feels uncomfortable.”
- Interviewer : “How did your team respond?”
- Candidate : “I apologized. They respected that. It built trust.”
- Interviewer : “Sounds like it made you a better leader.”
- Candidate : “I’d like to think so. I became more human after that.”
- Interviewer : “That matters here. We value empathy as much as skill.”
Through this analysis, our text emotion AI doesn't just label emotions; it maps a progressive emotional arc: from disappointment and realization to approval and shared values. This mirrors the authentic emotional evolution often present in meaningful job interviews and provides a fuller picture of a candidate’s self-awareness and potential fit within a team culture.
See Hidden Emotion in Words with Imentiv Text API
From product pitches and CEO interviews to therapy sessions and job interviews, every transcript carries an emotional fingerprint that often goes unnoticed. Imentiv’s Text Emotion AI brings these silent signals to light, revealing doubt where confidence was assumed, admiration beneath hesitation, and desire behind uncertainty. By transforming plain transcripts into emotionally intelligent insights, we’re not just decoding language; we’re uncovering intention and connection.
This is just the beginning. In Part 2 , we’ll explore more real-world use cases, including courtroom interactions, support group transcripts, user research, and coaching debriefs, each offering new dimensions for emotion-aware decision-making.
Ready to uncover what your transcripts are saying?
Stay tuned, and start seeing text through the lens of Emotion AI.