Your customers are already telling you what to fix — in support tickets, reviews, survey comments, chat logs, social posts, and sales call notes. The problem is volume. Nobody can read tens of thousands of comments, so most of that feedback goes unused, and decisions get made on a handful of anecdotes.
Our text analytics services turn large volumes of written feedback into clear, measurable insights. We find the main themes, measure sentiment, track how topics change over time, extract mentions of products and features, and connect what people say to outcomes such as churn, ratings, or sales — delivered as reports, dashboards, or ongoing automated analysis.
What are text analytics services? Text analytics services analyze large volumes of unstructured text — such as reviews, survey comments, support tickets, emails, chats, and social media — to extract insights. Techniques include topic and theme discovery, sentiment and emotion analysis, classification, entity and keyword extraction, trend analysis, and LLM-based summarization, with results quantified and linked to business metrics in reports and dashboards.
Text Analytics at a Glance
Question | Answer |
|---|---|
What it is | Turning large volumes of written feedback and text into measurable insights |
Core deliverables | Theme and topic analysis, sentiment, classification, entity extraction, trend tracking, insight reports, dashboards, automated pipelines |
Text sources | Reviews, survey comments, support tickets, chats, emails, social media, call transcripts, internal documents |
Techniques | Topic modeling, classification models, sentiment analysis, entity extraction, LLM-based theme extraction and summarization |
Output | Insight reports, dashboards, tagged data sets, automated classification in your systems |
Engagement models | One-time analysis, recurring voice-of-customer reporting, automated text analytics pipeline |
How This Differs From Related Services
Service | Focus | Page |
|---|---|---|
Text analytics | Insights from text for business decisions | This page |
Text annotation | Human labeling of text to train AI models | |
NLP development | Building NLP systems and applications |
Common Use Cases
Voice of the customer — main themes and sentiment across reviews, surveys, and tickets
Support ticket analysis — top issues, root causes, and trends by product or channel
Product feedback — feature requests and pain points ranked by frequency and impact
Churn analysis — what customers who leave were complaining about beforehand
Review monitoring — your ratings and themes compared with competitors
Employee feedback — themes in engagement survey comments and exit interviews
Sales call insights — objections, competitor mentions, and buying signals from transcripts
Our Text Analytics Services
Theme and topic discovery
We identify the main themes in your text, combining statistical topic modeling, LLM-based theme extraction, and human review to produce a clear, stable set of themes.
Sentiment and emotion analysis
We measure sentiment overall and by theme, so you can see not just what people mention but how they feel about it.
Classification
We classify text into your categories — issue type, product, urgency, department — and can automate this for new text as it arrives.
Entity and keyword extraction
We extract mentions of products, features, competitors, locations, and other entities, and count them over time.
Trend and change analysis
We track how themes and sentiment change week by week, and flag emerging issues early.
Linking text to outcomes
We connect themes to ratings, churn, renewals, or sales, to show which issues matter most to the business.
Summarization
We produce concise, accurate summaries of large volumes of comments, with representative quotes.
Automated pipelines
We set up ongoing analysis that processes new feedback automatically and updates dashboards. For processing documents at scale, see unstructured data processing.
How It Works
Step 1: Define the questions
We agree which decisions the analysis should support and which text sources to include.
Step 2: Collect and prepare
We gather and clean the text, removing duplicates, noise, and personal data where needed.
Step 3: Discover themes
We build a theme framework and review it with you.
Step 4: Analyze
We measure themes, sentiment, entities, and trends, and link them to outcomes.
Step 5: Report
We deliver insights with priorities, examples, and recommendations.
Step 6: Automate (optional)
We set up recurring analysis and dashboards for new text.
What You Get
A clear theme framework for your feedback
Volume and sentiment by theme, segment, and time
Priority issues linked to business outcomes
Representative quotes for each key theme
Dashboards for ongoing tracking
Tagged data you can use in other systems
Who This Is For
Customer experience teams | Product teams | Support leaders | Marketing and research teams |
|---|---|---|---|
Need a clear view of what customers say across channels | Need feature requests and pain points ranked | Need top issues and root causes from tickets | Need review, brand, and competitor insights |
Why Work With Codersarts
Automated text analysis can produce vague or misleading themes if it isn't checked. We combine LLMs and NLP models with human review, validate themes against real examples, and quantify results so you can prioritize with confidence. Our analysts then connect findings to business decisions through our data analytics consulting practice.
Frequently Asked Questions
What is text analytics?
Text analytics is the use of statistical, NLP, and AI techniques to extract themes, sentiment, entities, and trends from large volumes of written text, so the results can be measured and acted on.
What kinds of text can you analyze?
Reviews, survey comments, support tickets, chat logs, emails, social media posts, call transcripts, and internal documents.
Do you use LLMs for text analytics?
Yes, where they help — especially for theme extraction and summarization. We combine them with classification models and human review so results are accurate and consistent.
How accurate is sentiment analysis?
Accuracy depends on the text and domain. We validate sentiment results on samples of your data and refine the approach where needed, especially for sarcasm and domain-specific language.
Can you analyze text in other languages?
Yes, depending on the languages involved. We confirm language coverage during scoping.
How long does a text analytics project take?
A one-time analysis typically takes one to a few weeks, depending on volume and sources. Automated pipelines take longer to set up and then run continuously.
How much do text analytics services cost?
Cost depends on text volume, number of sources, depth of analysis, and whether you need ongoing automation. We quote after reviewing samples.
Related Data Services
Survey Analysis — full analysis of survey data, including open-ended answers
Audio Annotation — transcribe calls before analyzing them
Business Intelligence — combine text insights with other KPIs
AI Data Services — prepare text data for AI systems
Ready to Hear What Your Customers Are Saying?
Tell us which text sources you have and what you want to learn. We'll show you what the analysis would reveal and what it would take.
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