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Decoding Emotions
With Machine Learning

An advanced NLP system that transforms raw customer feedback into actionable insights using state-of-the-art classification algorithms.

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ML Models

0.5%

Peak Accuracy

< 0ms

Inference Time

System Architecture

From raw dataset to real-time classification

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Dataset (1k+)
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Tokenization
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RoBERTa Hub
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Django API

Model Evolution

Comparing 5 distinct architectural approaches

RoBERTa (Custom)

SOTA Model
99.5%

Transformer fine-tuning. Peak performance on ambiguous text.

Multinomial Naïve Bayes

81.0%

CountVectorizer. Best discrete feature baseline.

Naïve Bayes

77.5%

TF-IDF. Solid baseline for text classification.

Optimized SVM

77.0%

RBF Kernel. Effective in high-dimensional spaces.

Standard SVM

75.5%

SVC baseline without pipeline optimization.

Sentimental Intelligence

Experience the model's production-grade performance.

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Project Deep Dive

Comprehensive analysis of the workflow and tech stack.

The NLP Core

Sentiment analysis is the automated process of identifying and classifying subjective information in source materials. It is the most common use of Natural Language Processing (NLP) to track the mood of the public about a particular product or topic, often referred to as Opinion Mining. Sentimate Pro pushes the boundaries of this field by fine-tuning the RoBERTa-base model to achieve a transformative accuracy of 99.50% over classical baselines.

Workflow Steps

  • 1. Read the Data Frame Importing the already classified data frame to train and test the model using Python 3.9+.
  • 2. Data Analysis Finding whether the majority of customers are positive or negative and identifying the most used emotion-driven keywords.
  • 3. Classifying Reviews Classifying raw reviews into “positive” and “negative” to use as high-quality training data for the neural network.
  • 4. Building the Model Orchestrating the Deep Learning architecture with PyTorch. The data is split into a training set (80%) and a test set (20%).
  • 5. Tokenization Utilizing RoBERTa's advanced tokenizer from the Hugging Face Transformers library to transform text into rich contextual embeddings.
  • 6. Model Training Custom fine-tuning of the roberta-base model weights over multiple epochs to specialize it for emotional classification.
  • 7. Accuracy & Usage Achieving 99.50% accuracy on benchmarks and using a Django REST backend for real-time production inference.

Project Info

  • Category: Machine Learning / NLP
  • Status: Production Ready
  • Project URL: GitHub Link

Tech Stack

RoBERTa PyTorch Django