Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Islam, Md. Jahidul; Datta, Ratri; Iqbal, Anindya

doi:10.1016/j.eswa.2023.119919

Cited by 8 publications

(2 citation statements)

References 39 publications

(36 reference statements)

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“…Researchers and developers can derive technological advantages from utilizing review data sourced from the Google Play Store in contrast to other application marketplaces. This preference is rooted in the subsequent factors [ 35 , 36 ]: (1) timely data availability: Google Play Store reviews are frequently accessible in real time or near real time, facilitating the analysis of user feedback and app performance with the most current data; this immediacy proves invaluable for monitoring app sentiment and promptly addressing emerging issues or trends. (2) An abundance of data: the Google Play Store, boasting an extensive user base and an extensive collection of applications, generates substantial quantities of review data.…”

Section: Methods and Resultsmentioning

confidence: 99%

Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application

Kim,

Lee

2023

Behavioral Sciences

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This research investigates the factors influencing user satisfaction and dissatisfaction in fitness mobile applications. It employs Herzberg’s two-factor model through text mining to classify Fitbit mobile app attributes into satisfiers and dissatisfiers. The Fitbit app was chosen due to its prevalence in the United States. The study analyzes 100,000 English reviews from the Fitbit app on the Google Play Store, categorizing attributes. It identifies three dissatisfying categories (functional, compatibility, paid services) and three satisfying categories (gratification, self-monitoring, self-regulation), comprising 25 sub-attributes. This classification offers in-depth insights into what drives user contentment or discontent with fitness apps. The findings contribute to the fitness app domain by applying text-mining and Herzberg’s model. Researchers can build upon this foundation, and practitioners can use it to enhance app experiences. However, this research relies on user reviews, often lacking comprehensive explanations. This limitation may hinder a profound understanding of the underlying psychological aspects in user sentiments. Nonetheless, this study takes strides toward optimizing fitness apps for users and developers.

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Section: Methods and Resultsmentioning

confidence: 99%

Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application

Kim,

Lee

2023

Behavioral Sciences

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“…It is widely used in natural language processing (NLP), language translation and sequence prediction. Islam et al (2023) used a hybridization model of RNN and LSTM for sentiment analysis of user ratings in an online meeting app. Yang (2022) applied LSTM to the field of intelligent translation of English.…”

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confidence: 99%

An electricity load forecasting model based on multilayer dilated LSTM network and attention mechanism

Wang,

Jiang,

Wang

et al. 2023

Front. Energy Res.

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From national development to daily life, electric energy is integral to people’s lives. Although the development of electricity should be expected, expansion without restriction will only result in energy waste. The forecasting of electricity load plays an important role in the adjustment of power enterprises’ strategies and the stability of power operation. Recently, the electricity-related data acquisition system has been perfected, and the available load information has gradually reached the minute level. This means that the related load series lengthens and the time and spatial information of load become increasingly complex. In this paper, a load forecasting model based on multilayer dilated long and short-term memory neural network is established. The model uses a multilayer dilated structure to extract load information from long series and to extract information from different dimensions. Moreover, the attention mechanism is used to make the model pay closer attention to the key information in the series as an intermediate variable. Such structures can greatly alleviate the loss in the extraction of long time series information and make use of more valid historical information for future load forecasting. The proposed model is validated using two real datasets. According to load forecasting curves, error curve, and related indices, the proposed method is more accurate and stable in electricity load forecasting than the comparison methods.

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Contrastive learning enhanced deep neural network with serial regularization for high-dimensional tabular data

Wu¹,

Zhu²,

Wang³

2023

Expert Systems with Applications

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Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

Cited by 8 publications

References 39 publications

Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application

Unpacking the Drivers of Dissatisfaction and Satisfaction in a Fitness Mobile Application

An electricity load forecasting model based on multilayer dilated LSTM network and attention mechanism

Contrastive learning enhanced deep neural network with serial regularization for high-dimensional tabular data

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