Sentiment-Analysis-Customer-Reviews Open-source Model - Analyze the Positive and Negative Sentiments of Amazon Review Texts for Free

Sentiment Analysis Customer Reviews

Developed by dnzblgn

A sentiment analysis model based on Amazon customer reviews, capable of classifying text as positive or negative sentiment.

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #High-accuracy sentiment analysis #E-commerce review classification #DeBERTa optimization

Downloads 1,819

Release Time : 10/3/2024

Model Overview

This model uses the microsoft/deberta-v3-base architecture, fine-tuned on the Amazon reviews dataset, specifically designed for analyzing the sentiment tendencies of customer reviews.

Model Features

High accuracy

Achieves 98% accuracy on the Amazon reviews dataset.

Domain-specific optimization

Specially optimized for e-commerce customer review scenarios.

Efficient inference

Based on the DeBERTa-v3-base architecture, balancing performance and efficiency.

Model Capabilities

Text sentiment classification

Customer review analysis

Sentiment tendency judgment

Use Cases

E-commerce analysis

Product review analysis

Automatically analyzes the sentiment tendencies of customer reviews on products.

Accurately identifies 98% of positive/negative reviews.

Service quality monitoring

Identifies service issues from customer feedback.

High recall rate (99%) ensures no negative reviews are missed.

Market research

Consumer sentiment analysis

Large-scale analysis of overall consumer sentiment towards brands or products.

🚀 Sentiment Analysis for Customer Reviews

This project focuses on text classification, specifically sentiment analysis of customer reviews. It uses a pre - trained model and is trained on Amazon reviews data to distinguish between positive and negative sentiments.

🚀 Quick Start

from transformers import pipeline

classifier = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews")
result = classifier("The product didn't arrive on time and was damaged.")
print(result)

✨ Features

Sentiment Mapping: It maps sentiment labels to numerical values, where 1 represents "Negative" and 0 represents "Positive".
Trained on Real - world Data: The model is trained on the McAuley - Lab/Amazon - Reviews - 2023 dataset, which contains real customer reviews from Amazon.
High - performance Metrics: Achieves high accuracy, precision, recall, and F1 - score in binary sentiment classification.

📦 Installation

Since the library used is transformers, you can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="dnzblgn/Sentiment-Analysis-Customer-Reviews")
result = classifier("The product didn't arrive on time and was damaged.")
print(result)

Advanced Usage

You can use the model in a more complex application, such as integrating it into a web service for real - time sentiment analysis of incoming customer reviews.

📚 Documentation

Training Details

The model was trained on the McAuley - Lab/Amazon - Reviews - 2023 dataset. This dataset contains labeled customer reviews from Amazon, focusing on two primary categories: Positive and Negative.

Training Hyperparameters

Property	Details
Model	microsoft/deberta - v3 - base
Learning Rate	3e - 5
Epochs	6
Train Batch Size	16
Gradient Accumulation Steps	2
Weight Decay	0.015
Warm - up Ratio	0.1

Evaluation

The model was evaluated using a subset of the Amazon reviews dataset, focusing on the binary classification of text as either positive or negative.

Metrics

Metric	Value
Accuracy	0.98
Precision	0.98
Recall	0.99
F1 - Score	0.98

📄 License

This project is licensed under the Apache - 2.0 license.

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