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  license: bsd-3-clause
 
 
 
 
 
 
 
 
 
 
 
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  license: bsd-3-clause
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+ language:
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+ - en
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+
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+ tags:
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+ - sentiment
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+ - bert
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+ - sentiment-analysis
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+ - transformers
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+
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+ pipeline_tag: text-classification
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  ---
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+ > Authors : GRP209
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+ # User Comment Sentiment Analysis
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+ This model aims to analyze user comments on products and extracting the expressed sentiments.
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+ User ratings on the internet do not always provide detailed qualitative information about their experience.
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+ Therefore, it is important to go beyond these ratings and extract more insightful information that can help a brand improve their product or service.
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+ # Objective
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+ The model utilizes the BERT architecture and is trained on a dataset of user comments with sentiment labels.
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+ The model is capable of analyzing comments and extracting sentiments such as **positive**, **negative**, or **neutral**.
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+ # Features
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+ **Sentiment Classification**: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion.
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+ **Improvement Suggestions**: In cases where a comment expresses a negative or neutral sentiment, the model suggests an improved version of the text with a more positive sentiment.
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+ This can help businesses understand consumer reactions and identify areas for product or service improvement.
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+
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+ # Usage
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+ To use this sentiment analysis system, follow these steps:
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+ - Install the required dependencies by running the command pip install -r requirements.txt.
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+ - Once the training is complete, the best-trained model will be saved in the best_model_state.bin file.
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+ - To make predictions on new comments, use the analyze_sentiment(comment_text) function, replacing comment_text with the actual comment text to analyze.
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+ - The model will return the sentiment expressed in the comment.
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+ - To suggest an improved version of a comment, use the suggest_improved_text(comment_text) function.
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+ - If the comment expresses a negative or neutral sentiment, the function will generate an improved version of the text with a more positive sentiment. Otherwise, the original text will be returned without modification.