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Natural Language Processing (NLP)

Sentiment & Intent Analysis

Millions of customer reviews, social media posts, and support tickets contain valuable business intelligence that humans cannot process at scale. We build NLP pipelines that automatically read, categorize, and score the emotional sentiment and underlying intent of every text your customers write.

Aspect-Based SentimentSocial ListeningIntent ClassificationVoice of Customer
100k+
Reviews Analyzed Daily
Built a pipeline that processes App Store and Google Play reviews in real-time for a major gaming studio.
92%
Classification Accuracy
Achieved near-human accuracy in routing complex technical support emails to the correct engineering tier.
Expert Led
Arsalan Abbas
NLP Data Scientist
Voice of Customer AnalyticsNLP Specialists
Capabilities

Core Features

Aspect-Based Sentiment

Not just 'this review is positive', but 'the user loved the battery life (positive) but hated the customer service (negative)' all from a single paragraph.

Intent Classification

Automatically routing incoming emails based on intent (e.g., classifying an email as 'Refund Request' vs 'Technical Support' before a human reads it).

Multilingual Analysis

Applying sentiment models across dozens of languages simultaneously, allowing global brands to track localized sentiment trends.

Trend & Spike Alerting

Real-time dashboards that trigger executive alerts if negative sentiment regarding a specific product suddenly spikes on Twitter or Reddit.

Implementation

Our Process

01

Data Aggregation

Week 1

Connecting APIs to ingest your unstructured text data from Zendesk, App Store reviews, Trustpilot, and social listening tools.

02

Taxonomy Definition

Week 2

Working with your CX team to define the exact 'Intents' and 'Aspects' the model needs to look for (e.g., 'Pricing', 'UI Bug', 'Shipping Delay').

03

Model Fine-Tuning

Week 3-4

Taking a base NLP model (like RoBERTa or an LLM) and fine-tuning it on a small set of manually labeled examples from your specific domain.

04

Pipeline Architecture

Week 5

Deploying the inference pipeline using Kafka/Spark to process millions of incoming text records per day efficiently.

05

Dashboard & BI Integration

Week 6

Visualizing the output in PowerBI or Tableau so executives can filter sentiment by product, region, or time period.

Tech Stack

Technologies We Use

Hugging Face (RoBERTa)
Core NLP Classification
OpenAI API
Few-Shot Intent Extraction
Apache Kafka / Spark NLP
High-Volume Processing
Elasticsearch
Text Indexing
Tableau
Sentiment Visualization
Common Questions

FAQ

Can't we just search for negative words like 'bad' or 'terrible'?

Is this better than the sentiment tool built into our CRM?

How do you handle industry-specific jargon?

Ready to Innovate?

Accelerate Your Business with
Sentiment & Intent Analysis

Book a free strategy call. We'll scope the exact requirements for your use case and walk you through our implementation approach.

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