hybrid

language: en license: mit tags:

• sentiment-analysis
• aspect-based-sentiment datasets:
• custom metrics:
• accuracy

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📊 Hybrid Aspect-Based Sentiment Analysis Model

A Hybrid Approach for Aspect-Based Sentiment Analysis (ABSA) that combines transfer learning, syntactic dependency parsing, and weak supervision to extract fine-grained sentiment insights from text.

🚀 Overview

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing (NLP) task that identifies specific aspects (features) in a sentence and determines the sentiment polarity associated with each aspect.

Unlike traditional sentiment analysis, which assigns a single sentiment to the whole sentence, ABSA provides fine-grained insights.

Example: Input: "The food was amazing but the service was slow."

Output:

• Food → Positive
• Service → Negative 🧠 Model Architecture

This project implements a hybrid approach inspired by recent research combining:

🔹 Pretrained Transformer Models (for contextual understanding) 🔹 Syntactic Dependency Parsing (for better aspect extraction) 🔹 Weakly-Supervised Learning (reduces dependency on labeled data)

The hybrid design leverages both LLM-based semantic understanding and rule-based linguistic structure, improving aspect detection accuracy.

⚙️ Features ✅ Aspect Term Extraction (ATE) ✅ Aspect Sentiment Classification (ASC) ✅ Hybrid (Rule + Deep Learning) Approach ✅ Domain Adaptable ✅ Reduced Need for Large Labeled Datasets 📂 Model Details Attribute Description Task Aspect-Based Sentiment Analysis Framework Hugging Face Transformers Approach Hybrid (Transfer Learning + Dependency Parsing) Input Text sentence Output Aspect + Sentiment pairs 🛠️ Installation pip install transformers torch 🔧 Usage from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Artengus/hybrid"

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "The battery is good but the camera is poor." aspects = ["battery", "camera"]

results = {}

for aspect in aspects: inputs = tokenizer(text, aspect, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits predicted_class_id = logits.argmax().item() results[aspect] = model.config.id2label[predicted_class_id]

print(results) 📊 How It Works

The pipeline typically follows:

Aspect Candidate Extraction Aspect Filtering (Dependency + Model) Sentiment Classification per Aspect

This multi-stage approach improves performance compared to single-stage models.

📈 Applications 🛒 Product Review Analysis 📱 App Feedback Mining 🏢 Customer Experience Analytics 📊 Market Research 🧠 Opinion Mining