Datasets:

sealuzh
/

app_reviews

---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
pretty_name: AppReviews
dataset_info:
  features:
  - name: package_name
    dtype: string
  - name: review
    dtype: string
  - name: date
    dtype: string
  - name: star
    dtype: int8
  splits:
  - name: train
    num_bytes: 32768731
    num_examples: 288065
  download_size: 13207727
  dataset_size: 32768731
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Home Page](https://github.com/sealuzh/user_quality)
- **Repository:** [Repo Link](https://github.com/sealuzh/user_quality)
- **Paper:** [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf)
- **Leaderboard:
- **Point of Contact:** [Darshan Gandhi]([email protected])

### Dataset Summary

It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)

### Supported Tasks and Leaderboards

The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these
apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective.

### Languages

The dataset is a monolingual dataset which has the messages English.

## Dataset Structure

### Data Instances

The dataset consists of a message in English.

{'package_name': 'com.mantz_it.rfanalyzer',
 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.",
 'date': 'October 12 2016',
 'star': 4}

### Data Fields

* package_name : Name of the Software Application Package
* review : Message of the user 
* date : date when the user posted the review 
* star : rating provied by the user for the application

### Data Splits

There is training data, with a total of : 288065

## 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

With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. 

### Discussion of Biases

The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here 

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio)

### Licensing Information

[More Information Needed]

### Citation Information

```
@inproceedings{grano2017android,
  author = {Grano, Giovanni and Di Sorbo, Andrea and Mercaldo, Francesco and Visaggio, Corrado A. and Canfora, Gerardo and Panichella, Sebastiano},
  title = {Android apps and user feedback: a dataset for software evolution and quality improvement},
  year = {2017},
  isbn = {9781450351584},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3121264.3121266},
  doi = {10.1145/3121264.3121266},
  abstract = {Nowadays, Android represents the most popular mobile platform with a market share of around 80\%. Previous research showed that data contained in user reviews and code change history of mobile apps represent a rich source of information for reducing software maintenance and development effort, increasing customers' satisfaction. Stemming from this observation, we present in this paper a large dataset of Android applications belonging to 23 different apps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews and more than 450,000 user feedback (extracted with specific text mining approaches). Furthermore, for each app version in our dataset, we employed the Paprika tool and developed several Python scripts to detect 8 different code smells and compute 22 code quality indicators. The paper discusses the potential usefulness of the dataset for future research in the field.},
  booktitle = {Proceedings of the 2nd ACM SIGSOFT International Workshop on App Market Analytics},
  pages = {8–11},
  numpages = {4},
  keywords = {App Reviews, Mobile Applications, Software Maintenance and Evolution, Software Quality},
  location = {Paderborn, Germany},
  series = {WAMA 2017}
}
```

### Contributions

Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.