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Dataset Card for Dataset Name
Dataset Description
- Homepage:
- https://github.com/AARSynth/Dataset
- Repository:
- https://github.com/AARSynth/Dataset
- Paper:
- App-Aware Response Synthesis for User Reviews. Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
- Point of Contact:
- Umar Farooq ([email protected])
- Abubakar Siddique ([email protected])
Dataset Summary
AARSynth is a large-scale app review dataset. There are 570K review-response pairs and more than 2 million user reviews for 103 popular applications.
Supported Tasks and Leaderboards
Question Answer Response Generation
Languages
English
How to use the dataset?
from datasets import load_dataset
import pandas as pd
# load the dataset
mbr_data = load_dataset('recmeapp/AARSynth', data_dir='replies')
# Save dataset to .csv file for creating pandas dataframe
mbr_data['train'].to_csv('./mbr_data.csv', sep='***')
# Convert to pandas dataframe
aarsynth_df = pd.read_csv('./mbr_data.csv', sep='***')
# How many interactions are there in the AARSynth dataset?
print(f'There are {len(aarsynth_df)} interactions in AARSynth dataset.')
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Umar Farooq and A.B. Siddique
Licensing Information
[More Information Needed]
Citation Information
- App-Aware Response Synthesis for User Reviews. Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
Contributions
[More Information Needed]