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--- |
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license: unknown |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 1M<n<10M |
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--- |
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# About Dataset |
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This dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for training fastText models for sentiment analysis. |
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The dataset is based on real business data at a reasonable scale, which can be trained on a modest laptop in minutes. |
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## Content |
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The fastText supervised learning tutorial requires data in this format: |
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`__label__<X> __label__<Y> ... <Text>` |
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- `X` and `Y` are the class names, without quotes and all on one line. |
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- In this dataset, the classes are `__label__1` and `__label__2`, with only one class per row. |
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- `__label__1` corresponds to 1- and 2-star reviews, while `__label__2` corresponds to 4- and 5-star reviews. |
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- 3-star reviews (neutral sentiment) are excluded from the dataset. |
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- Review titles are prepended to the text, followed by a colon and a space. |
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- Most reviews are in English, with a few in other languages like Spanish. |
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## Source |
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The data was obtained from Xiang Zhang's Google Drive directory in .csv format, which was then adapted for use with fastText. |
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## Training and Testing |
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Follow the instructions in the fastText supervised learning tutorial to set up the directory. |
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### Training |
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To train the model, use: |
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```bash |
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./fasttext supervised -input train.ft.txt -output model_amzn |
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``` |
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This should take a few minutes. |
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To test: |
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```bash |
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./fasttext test model_amzn.bin test.ft.txt |
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``` |
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Expect precision and recall of 0.916 if all is in order. |
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## Acknowledgments |
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Dataset obtained from https://www.kaggle.com/datasets/bittlingmayer/amazonreviews/data |
