How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

Guzmán, Emitzá; Maalej, Walid

doi:10.1109/re.2014.6912257

Cited by 477 publications

(422 citation statements)

References 20 publications

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“…They have proposed a simple "FeatureWordExtension" algorithm to include NLP mined features into LDA to expand the original feature set. Emitza Guzman and Walid Maalej extracted [14] product features from online reviews by supplying fine grained features obtained from NLP analysis with sentiment scores of each review to LDA model. This model with weighted average formula identifies high-level features with sentiment scores.…”

Section: Related Workmentioning

confidence: 99%

A Combined Approach for Effective Features Extraction from Online Product Reviews

Santosh¹

2018

IJEME

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Today E-commerce websites provide customers with the needed product information by giving various kinds of services to choose from. One such service is to allow the customer to read the end user online reviews. Online reviews contain features which are useful for the analysis in opinion mining. Converting these unstructured reviews into useful information require extracting the product features from them. Natural Language Processing (NLP) based technique extracts various kinds of product features including the low frequency features. Topic Modeling based approach also identifies specific product features from the online reviews. The effective number of product features is made available to the customer when these two approaches are combined. This results in the expanded product feature set so that the customer makes wise decisions without having to compromise on the feature set.

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Section: Related Workmentioning

confidence: 99%

A Combined Approach for Effective Features Extraction from Online Product Reviews

Santosh¹

2018

IJEME

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“…They conducted their study on Android apps. Guzman and Maalej combined LDA with sentiment analysis [10]. Their study outlined an automated approach for extracting reviews that contain feature-related information for requirements evolution tasks.…”

Section: Analyzing Star Ratings and Reviews Of Appsmentioning

confidence: 99%

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

Bezemer

Hassan

2016

Preprint

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How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the software quality of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 68% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by Abstract How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the software quality of their app.In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobil...

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“…Prahalad et al [21] introduced the idea of co-creation of value and meaning and found that users can exercise their influence in all parts of business system, can interact with the service providers to co-create value. Guzman et al [22] discussed user reviews and discussed how it can play a vital role in eliciting user requirements. Authors also proposed an approach to analyze explicit user feedback, submitted in form of informal text.…”

Section: The Four Elements Of Crowdsource Referenece Model: a Camentioning

confidence: 99%

“…Fu et al [25] analyzed over 13 million app reviews that why user like or dislike mobile apps. Guzman et al [22] have done sentiments analysis of app reviews to find out what user want to express about mobile apps. Sentiments of users excite them to perform this crowdsourcing task for their mental satisfaction, knowledge sharing and love for community.…”

Section: A the Crowd-usersmentioning

confidence: 99%

“…User as crowdsourcer may skim reviews for solution of problem that which app should be installed based on others users' experience [31]. Innovative ideas can be extracted from creative reviews of crowd and can develop improved version of app [13,22].User of app can be used for co-creation value when they write real time experience and can help in the process app development. Chen et al [32] discussed how informative "information" from raw reviews of app user in app store can aid developers to improve their apps for future updates.…”

Section:  Traditional Operation: Traditional Operation Meansmentioning

confidence: 99%

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A Case of Mobile App Reviews as a Crowdsource

Khalid¹,

Shehzaib²,

Asif³

2015

IJIEEB

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Abstract-Crowdsourcing is a famous technique to get innovative ideas and soliciting contribution from a large online community particularly in e-business. This technique is contributing towards changing the current business techniques and practices. It is also equally famous in analysis and design of m-business services. Mobile app stores are providing an opportunity for its users' to participate and contribute in the growth of mobile app market. App reviews given by users usually contain active, heterogeneous and real life user experience of mobile app which can be useful to improve the quality of app. Best to our knowledge, the strength of mobile app reviews as a crowdsource is not fully recognized and understood by the community yet. In this paper, we have analysed a crowdsourcing reference model to find out which features of crowdsource are present and are related to our case of mobile app reviews as a crowdsource. We have analyzed and discussed each construct of the reference model from the perspective of mobile app reviews. Moreover, app reviews as a crowdsourcing technique is discussed by utilizing the four pillars of the reference model: the crowd, the crowdsourcer, the crowdsourcing, and the crowdsourcing platform. We have also identified and partially validated certain constructs of the model and highlighted the significance of app reviews as a crowdsource based on existing literature. In this study, only one crowdsourcing reference model is used which can be a limitation of our study. The study can be further investigated and compared with other crowdsourcing reference models to get better insights of app reviews as a crowdsource. We believe that the understanding of app reviews as a crowdsourcing technique can lead to the further development of the mobile app market and can open further research opportunities.

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How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

Cited by 477 publications

References 20 publications

A Combined Approach for Effective Features Extraction from Online Product Reviews

A Combined Approach for Effective Features Extraction from Online Product Reviews

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

A Case of Mobile App Reviews as a Crowdsource

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