Aspect Based Sentiment Analyzer Using Bert

🚀 Aspect-Based Sentiment Analyzer using BERT

This is a fine - tuned BERT model for aspect - based sentiment analysis, which can classify sentiments related to specific aspects in text and offer insights into customer opinions.

🚀 Quick Start

Use the code below to get started with the model.

from transformers import pipeline

sentiment_analyzer = pipeline("text - classification", model="srimeenakshiks/aspect - based - sentiment - analyzer - using - bert")
result = sentiment_analyzer("The food was amazing, but the service was slow.", aspect="service")
print(result)

✨ Features

• Aspect - Based Analysis: It can accurately classify sentiments as positive, negative, or neutral for various product features or attributes mentioned in customer reviews or feedback.
• Trained on Diverse Data: Trained on the Stanford IMDB dataset, it can handle a wide range of sentiment expressions.
• Versatile Use: Can be used directly for sentiment classification or integrated into larger applications for customer feedback analysis.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline

sentiment_analyzer = pipeline("text - classification", model="srimeenakshiks/aspect - based - sentiment - analyzer - using - bert")
result = sentiment_analyzer("The food was amazing, but the service was slow.", aspect="service")
print(result)

Advanced Usage

There is no advanced usage example in the original document, so this part is skipped.

📚 Documentation

Model Details

Model Description

The Aspect - Based Sentiment Analyzer using BERT is a state - of - the - art natural language processing model. Leveraging the BERT architecture, it can understand contextual nuances and accurately classify sentiments for different product features or attributes in customer reviews or feedback. Trained on the Stanford IMDB dataset, it is valuable for businesses to enhance customer satisfaction and gather insights from user - generated content.

• Developed by: Srimeenakshi K S
• Model type: Aspect - Based Sentiment Analysis
• Language(s) (NLP): English
• License: MIT License
• Finetuned from model: BERT - base - uncased

Uses

Direct Use

The model can be used directly to classify sentiments in user - generated text based on specified aspects without additional fine - tuning. It is suitable for analyzing reviews, social media posts, and other forms of textual feedback.

Downstream Use

This model can be integrated into applications for customer feedback analysis, chatbots for customer service, or sentiment analysis tools for businesses aiming to improve their products and services based on customer input.

Out - of - Scope Use

The model may not perform well with text containing heavy sarcasm or nuanced expressions. It should not be used for critical decision - making processes without human oversight.

Training Details

Training Data

The model was trained on the IMDB dataset, which contains movie reviews labeled with sentiment (positive and negative). This diverse dataset allows the model to learn various sentiment expressions effectively.

Training Procedure

Preprocessing

Data preprocessing involved tokenization, padding, and normalization of text inputs to fit the BERT model requirements.

Training Hyperparameters

• Training regime: fp16 mixed precision

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated using the same dataset on which it was trained, ensuring consistent performance metrics and a reliable assessment of its aspect - based sentiment analysis capabilities.

Factors

The evaluation included various aspects such as product features, service quality, and user experience.

Metrics

Evaluation metrics included accuracy, precision, recall, and F1 - score, providing a comprehensive assessment of model performance.

Results

The model achieved an accuracy of 95% on the test dataset, demonstrating effectiveness in aspect - based sentiment classification. The results indicate that the model performs well across a range of aspects but may struggle with nuanced sentiment expressions.

Model Examination

Further interpretability work can be conducted to understand how the model makes its predictions, particularly focusing on attention mechanisms within BERT.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA GeForce RTX 4050
• Hours used: 20 hours
• Cloud Provider: AWS
• Compute Region: US - East
• Carbon Emitted: 3.5

Technical Specifications

Model Architecture and Objective

The model is based on the BERT architecture, designed to understand the context of words in a sentence and classify sentiments associated with different aspects effectively.

Compute Infrastructure

Hardware

• GPU: NVIDIA GeForce RTX 4050
• RAM: 16GB

Software

• Framework: PyTorch
• Library Version: Hugging Face Transformers version 4.44.2

Citation

BibTeX:

@model{srimeenakshiks2024aspect,
  title={Aspect - Based Sentiment Analyzer using BERT},
  author={Srimeenakshi K S},
  year={2024},
  publisher={Hugging Face}
}

APA: Srimeenakshi K S. (2024). Aspect - Based Sentiment Analyzer using BERT. Hugging Face.

Glossary

• Aspect - Based Sentiment Analysis (ABSA): A subfield of sentiment analysis that focuses on identifying sentiments related to specific features or aspects of a product or service.

Model Card Authors

• Author: Srimeenakshi K S

📄 License

This model is released under the MIT License.