What are the steps in sentiment analysis? A guide to understanding text-based insights

Your ticket queue might be full, but how are your users really feeling? Annoyed? Confused? Just fine? You won't know unless you ask — or better yet, listen in a smarter way.

That’s where sentiment analysis comes in. It adds emotional intelligence to your IT help desk support, so that IT teams can detect tone, urgency, and user frustration in real time. That, in turn, helps you prioritize the right tickets, respond more empathetically, and improve the support experience across the board.

Let’s walk through how it works and break down the key steps in sentiment analysis.

What is sentiment analysis?

Think of sentiment analysis as emotional radar for your help desk. It scans text — from tickets and chat logs to emails, surveys, and even social media — and identifies whether the sentiment is positive, neutral, or negative. Some tools even dig deeper, flagging specific emotions like frustration or satisfaction.

For IT teams, this is gold. You’re no longer flying blind or relying solely on CSAT scores or resolution times. You can see how people feel in the moment — and that insight can change how (and how fast) you respond.

And it works. Companies that use sentiment analysis to personalize responses see satisfaction rates jump from 65% to over 90%.

The key steps in sentiment analysis

Here’s a breakdown of the main steps in sentiment analysis, from raw text to real insights.

1. Data collection

Start by gathering the raw data. This could come from:

• Help desk tickets
• Live chat transcripts
• Email threads
• Social media posts
• Internal feedback surveys

The broader the data pool, the richer your analysis – and the resulting insights will be.

2. Preprocessing

This is the cleanup phase. Preprocessing helps transform messy, unstructured text into something machines can understand. It typically includes:

• Removing filler words (like “and,” “the,” or “is”)
• Standardizing capitalization
• Stripping out URLs or irrelevant symbols

Why this matters: Without proper cleaning, a phrase like “not bad” could get misread as negative — when it’s actually neutral or even mildly positive.

3. Classification

Once the data is clean, it’s time to classify sentiment. Is it positive, negative, or neutral? More advanced tools go further, detecting emotions like anger, urgency, or satisfaction.

There are two main approaches:

• Machine learning models (like Naive Bayes or BERT) learn from historical examples
• Rule-based systems apply fixed rules to identify tone based on keywords and patterns.

At Fixify, we use both — combining AI with real human oversight to catch things automation alone often misses, like sarcasm or subtle frustration.

4. Analysis

This is where things start to come together — your data transforms into insights. Dashboards and trend graphs help you visualize sentiment across different channels, time periods or ticket categories.

Let’s say you’ve just rolled out a software update. You can use sentiment data to track how users react — and if negative feedback is trending, you’ll know something needs fixing fast.

5. Interpretation

The magic isn’t in the numbers — it’s in what you do with them.

Example: You notice a spike in negative sentiment tied to VPN tickets. That’s your cue to investigate, fix the issue, and communicate clearly with users — before it affects productivity or erodes your users’ trust.

Sentiment analysis gives you early warning signals that might not appear in CSAT or resolution metrics until it’s too late.

Tools and techniques used in sentiment analysis

Here are the main components that make sentiment analysis work:

• Natural Language Processing (NLP): Helps machines understand the structure and meaning of human language.
• Machine Learning (ML): Trains models to spot sentiment patterns from past data and get smarter over time.
• Rule-based systems: Uses logic to tag sentiment based on predefined keywords or structures. It’s simpler but still useful for specific keyword-triggered cases.
• APIs and third-party tools: Think IBM Watson, Google Cloud NLP, and Amazon Comprehend.

That said, many of these tools can feel like black boxes. That’s why Fixify builds sentiment analysis directly into help desk reporting — so your team can actually see and act on what the data is telling you.

How IT teams use sentiment analysis day-to-day

Sentiment analysis helps support teams make faster, more informed decisions. For example, it can help you:

• Prioritize important tickets: Flag frustration so it doesn’t sit in the queue.
• Route smarter: Send emotionally charged issues to the right technicians faster.
• Improve your knowledge base: Spot repeat questions or misunderstood processes and build resources to prevent tickets.

Real-world example

Imagine Alex, a product manager, submits a ticket:

“Ever since the VPN update, I can’t access anything. It’s completely messed up my workflow.”

Sentiment analysis reads the high frustration and negative tone and then flags the ticket for priority routing. Instead of sitting in the queue, it jumps to Tier 2 — saving Alex hours of lost productivity and showing that your team is listening before someone has to escalate.

From reactive to proactive

Sentiment analysis transforms your support approach. Instead of reacting to complaints after the fact, you’re spotting patterns, catching problems early, and giving your team the context they need to respond like humans — not robots.

With Fixify, you don’t need a data science team to make it happen. We embed sentiment tracking into your day-to-day IT helpdesk process, giving you real-time emotional context about every ticket.

See what Fixify can do for your help desk.