Extracting Arguments Based on User Decisions in App Reviews

Kunaefi, Anang; Aritsugi, Masayoshi

doi:10.1109/access.2021.3067000

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…Data preprocessing is needed to get clean reviews as well as reviews that have done restoration on its words. Cleaning and restoration of words in the review are important to do because they can provide a strong foundation for the next modelling stage [26]. Six processes will be carried out on data preprocessing, namely as follows:  Data cleansing: In data cleansing, you will get reviews that are reliable and structurally clean.…”

Section: Data Preprocessingmentioning

confidence: 99%

Classifying product review quality based on semantic and structural features

Akhyar Firdaus,

Rolliawati,

Kunaefi

et al. 2023

IJEECS

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<p>Product reviews are written opinions submitted by consumers in assessing a product. The existence of product reviews is important because it can help consumers make better product purchasing decisions. But product reviews can also be unimportant if the quality of the information from the reviews is not helpful. This can be minimized if a classification is carried out to find out which reviews are helpful or not. For this to be achieved, this research will apply a support vector machine model using semantic and structural features to be able to classify review texts based on their characteristics. By applying the appropriate preprocessing stages, the final results show that the semantic features produce the highest F1-score value of 0.825. Whereas the structural features produce the highest F1-score value of 0.823. From this, it can be concluded that semantic features can be used to identify the characteristics of a review text that are helpful or not properly. This success also shows outstanding performance in classifying reviews as helpful or not compared to previous studies.</p>

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Section: Data Preprocessingmentioning

confidence: 99%

Classifying product review quality based on semantic and structural features

Akhyar Firdaus,

Rolliawati,

Kunaefi

et al. 2023

IJEECS

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“…Our paper search was performed until early 2020. We acknowledge that additional opinion mining tools and datasets have been released [19,23,39,82] and more performance comparisons have been conducted [21,23,60,81].…”

Section: Threats To Validitymentioning

confidence: 99%

Opinion Mining for Software Development: A Systematic Literature Review

Lin

Cassee

Serebrenik

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.

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“…It has been reported that online reviews by customers are ranked to be as trustworthy as personal recommendations. Second, reviews are essential for app developers, as any new design or features can consider user requirements as reflected in their reviews [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

Mubin

Cai

Mahmud

et al. 2021

IJERPH

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Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake.

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Extracting Arguments Based on User Decisions in App Reviews

Cited by 7 publications

References 52 publications

Classifying product review quality based on semantic and structural features

Classifying product review quality based on semantic and structural features

Opinion Mining for Software Development: A Systematic Literature Review

A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

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