Wikipedia English Text
URL
Change Log
| When | What |
|---|---|
| January 29th, 2016 | Donated by Phong Minh Vu |
Reference
Studies who have been using the data (in any form) are required to include the following reference:
@INPROCEEDINGS{7372063,
author={Vu, Phong Minh and Nguyen, Tam The and Pham, Hung Viet and Nguyen, Tung Thanh},
booktitle={Automated Software Engineering (ASE), 2015 30th IEEE/ACM International Conference on},
title={Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)},
year={2015},
pages={749-759},
keywords={Batteries;Data mining;Dictionaries;Energy consumption;Facebook;Mobile communication;Keyword;Opinion Mining;Review Analysis},
doi={10.1109/ASE.2015.85},
month={Nov},}
About the Data
Overview of Data
English text wikipedia dump
Paper Abstract
User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.