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--- |
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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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tags: |
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- code |
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size_categories: |
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- 1M<n<10M |
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--- |
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# Dataset Card for Dataset Name |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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This dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews. |
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### Supported Tasks and Leaderboards |
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Detecting app issues proactively by identifying prominent app reviews. |
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### Languages |
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English |
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## How to use the dataset? |
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``` |
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from datasets import load_dataset |
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import pandas as pd |
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# Load the dataset |
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dataset = load_dataset("recmeapp/thumbs-up") |
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# Convert to Pandas |
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dfs = {split: dset.to_pandas() for split, dset in dataset.items()} |
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dataset_df = pd.concat([dfs["train"], dfs["validation"], dfs["test"]]) |
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# How many rows are there in the thumbs-up dataset? |
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print(f'There are {len(dataset_df)} rows in the thumbs-up dataset.') |
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# How many unique apps are there in the thumbs-up dataset? |
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print(f'There are {len(dataset_df["app_name"].unique())} unique apps.') |
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# How many categoris are there in the thumbs-up dataset? |
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print(f'There are {len(dataset_df["category"].unique())} unique categories.') |
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# What is the highest vote a review received in the thumbs-up dataset? |
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print(f'The highest vote a review received is {max(dataset_df["votes"])}.') |
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``` |
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## Usage |
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This dataset was used for training the PPrior, a novel framework proposed in [this paper](https://ieeexplore.ieee.org/abstract/document/10020586). You can find the implementation in this [GitHub repository](https://github.com/MultifacetedNLP/PPrior). |
