Update README.md

README.md

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 pipeline_tag: text-classification
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-Sentiment Analysis Model for Hotel Reviews
-This model performs sentiment analysis on hotel reviews. The goal is to classify reviews into one of the three categories: Negative, Neutral, or Positive.
+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.
 
-Model Description
-This model is based on the BERT (Bidirectional Encoder Representations from Transformers) model, specifically bert-base-uncased.
+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.
 
-Training Procedure
-The model was trained on the TripAdvisor hotel reviews dataset. Each review in the dataset is associated with a rating from 1 to 5. 
-The ratings were converted to sentiment labels as follows:
+Features
+Sentiment Classification: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion.
 
-Ratings of 1 and 2 were labelled as 'Negative'
-Rating of 3 was labelled as 'Neutral'
-Ratings of 4 and 5 were labelled as 'Positive'
-The text of each review was preprocessed by lowercasing, removing punctuation, emojis, and stop words, and tokenized with the BERT tokenizer.
-
-The model was trained with a learning rate of 2e-5, an epsilon of 1e-8, and a batch size of 6 for 5 epochs.
-
-Evaluation
-The model was evaluated using a weighted F1 score. 
+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 the model, load it and use it to classify a review. For example:
-
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("<Group209>")
-model = AutoModelForSequenceClassification.from_pretrained("<Group209>")
+To use this sentiment analysis system, follow these steps:
 
-text = "The hotel was great and the staff were very friendly."
+Install the required dependencies by running the command pip install -r requirements.txt.
 
-encoded_input = tokenizer(text, truncation=True, padding=True, return_tensors='pt')
-output = model(**encoded_input)
-predictions = output.logits.argmax(dim=1)
+Once the training is complete, the best-trained model will be saved in the best_model_state.bin file.
 
-print(predictions)
+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.
 
-Limitations and Bias
-The model is trained on English data, so it might not perform well on reviews in other languages. 
-Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset.
+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.