metadata

task_categories:
  - text-classification
language:
  - en
tags:
  - code
size_categories:
  - 1M<n<10M

Dataset Card for Dataset Name

Dataset Description

Homepage:
Repository:
Paper:
Leaderboard:
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Dataset Summary

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.

Supported Tasks and Leaderboards

Detecting app issues proactively by identifying prominent app reviews.

Languages

English

How to use the dataset?

from datasets import load_dataset
import pandas as pd

# Load the dataset
dataset = load_dataset("recmeapp/thumbs-up")

# Convert to Pandas
dfs = {split: dset.to_pandas() for split, dset in dataset.items()}
dataset_df = pd.concat([dfs["train"], dfs["validation"], dfs["test"]])

# How many rows are there in the thumbs-up dataset?
print(f'There are {len(dataset_df)} rows in the thumbs-up dataset.')

# How many unique apps are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["app_name"].unique())} unique apps.')

# How many categoris are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["category"].unique())} unique categories.')

# What is the highest vote a review received in the thumbs-up dataset?
print(f'The highest vote a review received is {max(dataset_df["votes"])}.')

Usage

This dataset was used for training the PPrior, a novel framework proposed in this paper. You can find the implementation in this GitHub repository.