Sentiment Analysis Model Amazon Reviews

sohan-ai

sentiment-analysis-model-amazon-reviews

This model is a fine-tuned Distilled BERT model for sentiment analysis on Amazon reviews. It is based on the distilbert-base-uncased model and uses a binary classification approach to predict whether a review is positive or negative. The model is trained on a dataset of Amazon reviews and evaluated using metrics such as accuracy, precision, recall, and F1 score. It is deployed on Hugging Face's model hub and can be downloaded and used through the Transformers library. The model provides an accurate and efficient way to perform sentiment analysis on Amazon reviews for tasks such as customer feedback analysis and sentiment monitoring.

Use cases

This sentiment analysis model for Amazon reviews has several potential use cases in various industries. It can be utilized for customer feedback analysis, allowing businesses to gather insights on customer satisfaction and identify areas for improvement. Market research teams can employ this model to analyze the sentiment of product reviews, helping them understand consumer preferences and make informed business decisions. Additionally, this model can be used for sentiment monitoring on social media platforms, enabling companies to track public opinion and respond to customer concerns in real-time. With its accurate and efficient sentiment analysis capabilities, this model has potential for integration into customer service platforms, review aggregator websites, and social media monitoring tools.

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Creator Models

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Test$?12

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Overview

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Creatorsohan-ai
Model NameSentiment Analysis Model Amazon Reviews
Description

Fine-Tuned Distilled BERT Model for Sentiment Analysis on Amazo...

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Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Runs41,699
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Cost per Run$-
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