| --- |
| language: |
| - en |
| tags: |
| - sentiment |
| - bert |
| - sentiment-analysis |
| - transformers |
| pipeline_tag: text-classification |
| --- |
| |
| User Comment Sentiment Analysis |
| This model aims to analyze user comments on products and extracting the expressed sentiments. |
| User ratings on the internet do not always provide detailed qualitative information about their experience. |
| Therefore, it is important to go beyond these ratings and extract more insightful information that can help a brand improve their product or service. |
|
|
| Objective |
| The model utilizes the BERT architecture and is trained on a dataset of user comments with sentiment labels. |
| The model is capable of analyzing comments and extracting sentiments such as positive, negative, or neutral. |
|
|
| Features |
| Sentiment Classification: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion. |
|
|
| 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. |
|
|
| Usage |
| To use this sentiment analysis system, follow these steps: |
|
|
| 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. |
