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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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- Transformateurs |
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pipeline_tag: text-classification |
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--- |
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Sentiment Analysis Model for Hotel Reviews |
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This model performs sentiment analysis on hotel reviews. The goal is to classify reviews into one of the three categories: Negative, Neutral, or Positive. |
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Model Description |
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This model is based on the BERT (Bidirectional Encoder Representations from Transformers) model, specifically bert-base-uncased. |
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Training Procedure |
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The model was trained on the TripAdvisor hotel reviews dataset. Each review in the dataset is associated with a rating from 1 to 5. |
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The ratings were converted to sentiment labels as follows: |
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Ratings of 1 and 2 were labelled as 'Negative' |
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Rating of 3 was labelled as 'Neutral' |
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Ratings of 4 and 5 were labelled as 'Positive' |
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The text of each review was preprocessed by lowercasing, removing punctuation, emojis, and stop words, and tokenized with the BERT tokenizer. |
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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. |
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Evaluation |
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The model was evaluated using a weighted F1 score. |
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Usage |
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To use the model, load it and use it to classify a review. For example: |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("<Group209>") |
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model = AutoModelForSequenceClassification.from_pretrained("<Group209>") |
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text = "The hotel was great and the staff were very friendly." |
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encoded_input = tokenizer(text, truncation=True, padding=True, return_tensors='pt') |
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output = model(**encoded_input) |
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predictions = output.logits.argmax(dim=1) |
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print(predictions) |
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Limitations and Bias |
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The model is trained on English data, so it might not perform well on reviews in other languages. |
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Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset. |
