Comprehensive Social Listening
Real-time monitoring of social media channels including Twitter and Meta, collecting customer feedback and identifying brand mentions.
Artificial IntelligenceData ScienceNLP, Machine Learning, Data Visualisation
Sentiment Analysis & Trend Prediction
Multilingual social listening and customer sentiment intelligence
Multilingual
Sentiment Detection
Real-Time
Social Listening
Predictive
Trend Forecasting
Overview
Reading what customers actually mean.
Traditional satisfaction metrics tell a brand what happened after the fact. They do not surface why sentiment moved, in which language, or what it predicts about the next quarter.
ABM Tech built a social intelligence system that monitors channels in real time, classifies sentiment across languages, and turns the volume into forecasts a marketing team can act on.
The Challenge
What we needed to solve.
The client operates in a complex, multilingual, global market and needed deep insight into customer perception — something traditional metrics could not provide.
The system had to analyse very large volumes of social media content, identify emerging trends early, and forecast patterns in customer behaviour rather than merely reporting on them.
The Solution
What we built.
The pipeline is built in Python, with spaCy handling the language processing and Prodigy driving the annotation loop that keeps model accuracy improving against real, messy data.
Sentiment classification goes beyond positive, negative and neutral to catch the nuanced cases — sarcasm and irony — that flip a reading's meaning and quietly corrupt naive models.
Advanced Sentiment Analysis
Multilingual detection of positive, negative and neutral sentiment, plus nuanced expressions such as sarcasm and irony.
Automated Categorisation
Feedback classified by topic and feature, with themes identified and prioritised automatically.
Dashboards and Reporting
Customisable visualisations with real-time updates and alerting on significant shifts.
Results
What shipped.
Expanded
Brand Reach
New audiences identified through social monitoring
Higher
Marketing ROI
Channel allocation driven by sentiment data
Automated
Team Workflow
Manual monitoring effort removed
- New audiences identified and competitor activity tracked through continuous social monitoring.
- Higher marketing return achieved by reallocating channel spend against data-driven insight.
- Marketing and customer service teams freed from manual monitoring for strategic work.
Technology
The stack.
Python · spaCy · Prodigy
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