What is sentiment analysis in AI?

The purpose of sentiment analysis is to turn messy, subjective text (reviews, chats, posts, emails) into clear signals you can actually act on.

You can think of it as a way to "quantify emotions" at scale that helps to:

Understand customer opinion
It lets you listen to customers at scale by converting unstructured text into measurable data about satisfaction, pain points, and brand perception.

Improve products and services
By spotting recurring negative themes in feedback, you can identify what's broken or confusing and prioritize fixes and new features.

Optimize marketing and campaigns
Analyzing sentiment around campaigns, messages, and channels shows what resonates, what annoys people, and where to adjust tone or positioning.

Track brand health
Ongoing sentiment tracking across reviews, social media, and forums helps you monitor how people feel about your brand, competitors, and trends in real time.

Enhance customer support
Support teams can use sentiment to understand urgency and frustration, prioritize angry customers, and even route tickets automatically based on tone.

Automate subjective evaluation
Instead of relying on ad-hoc, biased human reading, sentiment analysis gives a more consistent, scalable way to evaluate opinions.

Identify trends and patterns
Across large datasets, it reveals hidden patterns, like which features delight users, which changes triggered backlash, or which segments are most at risk of churn.

In short, the purpose of sentiment analysis is to transform scattered opinions into structured insight that improves customer experience, products, and decision-making.

Sentiment analysis uses natural language processing (NLP) and machine learning (ML) to teach software how to "read" text and decide whether it expresses positive, negative, or neutral sentiment. Most systems use one of three approaches: rule-based, machine learning–based, or a hybrid of both.

RULE-BASED SENTIMENT ANALYSIS

In a rule-based approach, the system relies on predefined rules and word lists (lexicons):

• It uses dictionaries of positive words ("affordable," "fast," "well-made") and negative words ("expensive," "slow," "poorly made").
• The software scans the text, looks for these words, and calculates an overall sentiment score based on which words appear, how often, and sometimes their weight.

Rule-based systems can work in near real time and are easier to understand, but they struggle with context, sarcasm, mixed sentiment, and new language they weren't explicitly programmed for.

ML SENTIMENT ANALYSIS

With the ML approach, the system learns from examples instead of fixed rules:

• Developers feed the model many text samples labeled as positive, negative, or neutral (and sometimes more fine-grained emotions).
• The model learns patterns from the words, their combinations, and their order to predict sentiment in new, unseen text.

Common ML techniques include:

• Naive Bayes – uses probability (Bayes' theorem) to estimate how likely a text is to belong to a sentiment class.
• Support Vector Machines (SVMs) – find the best boundary that separates positive vs. negative examples.
• Deep learning/neural networks – more advanced models (LSTMs, transformers) that can capture complex patterns and context.

ML-based systems are more adaptable and generally better at handling nuance, but they need training data, computing resources, and ongoing tuning.

HYBRID APPROACH

Hybrid sentiment analysis combines rule-based and ML methods:

• Rules and lexicons can handle simpler, high-precision cases and domain-specific terms.
• ML models handle context, complexity, and edge cases.

This typically delivers higher accuracy and flexibility but also requires more time, expertise, and resources to build and maintain.

Sentiment analysis is often used to improve customer experience by turning raw feedback into clear, actionable signals. So companies can use it to:

Monitor satisfaction in real time
Analyze reviews, support tickets, chat logs, and social media to see whether customers are feeling positive, neutral, or negative and spot drops in sentiment early.

Prioritize and route support more intelligently
Flag highly negative messages and route them to senior agents or specialized teams, while simple or neutral requests can go to standard queues or bots.

Reduce churn and rescue unhappy customers
Identify at-risk customers based on negative sentiment in emails, chats, or NPS comments, then trigger proactive outreach, discounts, or personalized support.

Improve products and services
Group feedback by topic (pricing, features, UX, delivery, support quality) and sentiment to see what frustrates customers most and what they love, then feed that into the product roadmap.

Personalize communication and marketing
Adjust tone, offers, and messaging based on how a customer feels about your brand, offer more education and reassurance for frustrated users, and provide more upsell/cross-sell opportunities for advocates.

Measure the impact of changes
After a new feature, campaign, or policy change, track sentiment over time to see whether customer experience is actually getting better or worse.

Implementing sentiment analysis means turning raw text (reviews, chats, social posts, tickets) into structured sentiment labels like positive, negative, or neutral. In practice, it happens in a few key stages:

1. Collect the data
   Gather text from relevant sources: social media, app reviews, NPS responses, support tickets, CRM notes, surveys, or call transcripts (after transcription).
2. Preprocess the text
   Clean and normalize the data by removing noise (HTML, extra symbols), tokenizing text into words or phrases, lowercasing, removing stop words, and optionally applying stemming or lemmatization so "running," "runs," and "ran" are treated as related forms.
3. Extract features
   Convert text into a machine-readable representation. This can be simple (bag-of-words, TF–IDF) or modern embeddings from transformer models (e.g., sentence vectors). For rule-based systems, this is where you define sentiment lexicons and patterns.
4. Classify sentiment
   With ML, train or fine-tune models on labeled examples of text with known sentiment.
   
   With rule-based methods, use dictionaries of positive/negative terms, plus rules for negation ("not great") and intensity ("very happy"), to score each text. Many production systems use a hybrid approach to balance speed, accuracy, and domain specificity.
5. Analyze and visualize results
   Aggregate sentiment by product, feature, channel, or customer segment. Visualize trends in dashboards to see where sentiment is improving, dropping, or signaling risk (like churn or PR issues).
6. Integrate into workflows
   Plug sentiment scores into real processes: prioritize angry tickets, trigger alerts for spikes in negative feedback, feed product roadmaps with recurring complaints, or personalize communication for promoters vs. detractors.

If you're just experimenting, off-the-shelf sentiment tools are fine. But if you need results tailored to your domain, languages, and workflows, you'll get much more value from a custom model that's trained on your data and integrated into your CRM or support stack.

That's exactly where our machine learning team comes in: we design, train, and deploy sentiment analysis solutions that handle real, messy data and work inside your existing systems, not just in demos.