Table of contents
Upskilling Made Easy.
NLP for Sentiment Analysis – Decoding Emotions from Text
Published 04 May 2025
992
5 sec read
Sentiment Analysis uses NLP to determine emotions in text—whether positive, negative, or neutral. Businesses use it for brand monitoring, customer feedback, and market research.
Rule-Based: Uses predefined keyword lists (e.g., "happy" = positive).
Machine Learning-Based: Trains models on labeled datasets.
Hybrid Approach: Combines both for better accuracy.
VADER (Valence Aware Dictionary for Sentiment Reasoning) – Good for social media.
TextBlob – Simple Python library for beginners.
BERT-based Models – High accuracy for complex texts.
Brand Monitoring: Tracking customer opinions on social media.
Customer Support: Prioritizing negative reviews.
Political Analysis: Gauging public opinion on policies.
Sarcasm & Contextual Meaning – Hard to detect.
Multilingual Sentiment Analysis – Requires language-specific models.
Emotion Detection (beyond just positive/negative).
Real-Time Sentiment Tracking for live feedback.
Image Suggestion:
Power BI is Microsoft’s powerful data visualization tool that transforms raw data into interactive dashboards and reports.
User-Friendly Drag-and-Drop Interface
Seamless Integration with Excel, SQL, and Azure
Real-Time Data Analytics
DAX (Data Analysis Expressions) – Advanced calculations.
Power Query – Data cleaning & transformation.
AI-Powered Insights – Automatic pattern detection.
Sales Forecasting
Supply Chain Optimization
Financial Reporting