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language: |
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- en |
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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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pipeline_tag: text-classification |
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--- |
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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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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. |
