| --- |
| license: bsd-3-clause |
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| language: |
| - en |
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| tags: |
| - sentiment |
| - bert |
| - sentiment-analysis |
| - transformers |
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| pipeline_tag: text-classification |
| --- |
| > 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. |
| This can help businesses understand consumer reactions and identify areas for product or service improvement. |
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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. |
| - Once the training is complete, the best-trained model will be saved in the best_model_state.bin file. |
| - To make predictions on new comments, use the analyze_sentiment(comment_text) function, replacing comment_text with the actual comment text to analyze. |
| - The model will return the sentiment expressed in the comment. |
| - To suggest an improved version of a comment, use the suggest_improved_text(comment_text) function. |
| - 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. |
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