首页/论文/1712.03430

arXiv 1712.034302017-12-09cs.CL

Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews

Indian Institute of Technology Madras（印度理工学院马德拉斯分校）

Sharmistha Dey

备注：: 12 pages

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

Understanding of customer sentiment can be useful for product development. On top of that if the priorities for the development order can be known, then development procedure become simpler. This work has tried to address this issue in the mobile app domain. Along with aspect and opinion extraction this work has also categorized the extracted aspects ac-cording to their importance. This can help developers to focus their time and energy at the right place.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews

Brawijaya University（布拉维贾亚大学）

User reviews have an essential role in the success of the developed mobile apps. User reviews in the textual form are unstructured data, creating a very high complexity when processed for sentiment analysis. Previous approaches that have been used often ignore the context of reviews. In addition, the relatively small data makes the model overfitting. A new approach, BERT, has been introduced as a transfer learning model with a pre-trained model that has previously been trained to have a better context representation. This study examines the effectiveness of fine-tuning BERT for sentiment analy…

2021-07-14cs.CLcs.LG

Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset

College of Science and Engineering, HBKU（哈马德·本·哈利法大学科学与工程学院）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Information Technology University（信息技术大学）；University of Nebraska（内布拉斯加大学）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；University of Trento（特伦托大学）；Shaheed Benazir Bhutto Women University（贝娜齐尔·布托女子大学）

Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing informat…

2021-03-01cs.CL

探索心理健康应用程序用户评论中的伦理问题：使用主题建模和情感分析

Exploring the Ethical Concerns in User Reviews of Mental Health Apps using Topic Modeling and Sentiment Analysis

School of Computing and Informatics（计算与信息学学院）；University of Louisiana at Lafayette（路易斯安那州立大学拉法叶分校）

本研究通过主题建模和情感分析，探讨心理健康应用用户评论中的伦理问题，揭示现有伦理原则的不足及新兴伦理挑战。

2026-02-04cs.CLcs.CYcs.HC

Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）；Singapore Management University（新加坡管理大学）；Monash University（蒙纳士大学）

Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing revi…

2020-08-23cs.CLcs.SE

一种多模型方法用于政府移动银行应用评论的英语-孟加拉语情感分类

A Multi-Model Approach to English-Bangla Sentiment Classification of Government Mobile Banking App Reviews

Data Science Research Lab, Department of Statistics, University of Rajshahi（数据科学研究实验室，统计系，拉贾沙希大学）；Department of Statistics, Mawlana Bhashani Science and Technology University（统计系，穆拉纳·巴希安尼科学与技术大学）；Department of Statistics, University of Rajshahi（统计系，拉贾沙希大学）

研究通过多模型方法分析四款孟加拉国政府银行应用的英语和孟加拉语评论，发现传统模型在情感分类中表现优于Transformer模型，且孟加拉语文本的准确性差距凸显了低资源语言模型开发的必要性。

2026-03-17cs.CL

移动应用中的大语言模型：实践、挑战与机遇

LLMs in Mobile Apps: Practices, Challenges, and Opportunities

University of Toronto（多伦多大学）

本研究构建149个Android应用数据集，探索LLM在移动应用中的集成实践，揭示设备限制、API管理等挑战，为未来研究提供方向。

2025-02-21cs.CLcs.SE

更多相关论文收起更多论文

SkillBot: Identifying Risky Content for Children in Alexa Skills

University of Virginia（弗吉尼亚大学）；New York University（纽约大学）；Colgate University（科尔盖特大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）

Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this paper, we first investigate "risky" child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot - a natural language processing (NLP)-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growin…

2022-06-02cs.CLcs.CRcs.HC