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Users' Sentiment Analysis toward National Digital Library of India: a Quantitative Approach for Understanding User perception

Authors: Sharma, Ritu; Gulati, Sarita; Kaur, Aman; Chakravarty, Rupak;

Users' Sentiment Analysis toward National Digital Library of India: a Quantitative Approach for Understanding User perception

Abstract

Sentiment analysis is also known as opinion mining. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. It is extremely used by business, educational organizations, and social media monitoring to gain the general outlook of the wide public regarding their product and policy. The current study looks for gaining insights into user reviews on the National Digital Library of India (NDLI) mobile app (android and iOS). For this purpose, sentiment analysis will be used. It yields an average of 3.64/5 ratings based on 11,861 reviews. The dataset includes a total of 4560 user reviews in which iOS and the android app have received 33 and 4527 reviews respectively as on 7th Sept 2021. AppBot and AppFollow analytics software is used to extract and collect user review information as raw data. The study shows the reviews of the NDLI mobile app as 2130 positive and 1808 negative sentiments for android & 6 positive and 22 negative sentiments for iOS. The overall sentiment score is found to be 66%. The results of the sentiment analysis show that Android users are more satisfied as compared to iOS users. The most frequent complaints made by the users are functional errors, feature requests and app crashes. Some of the major issues that users have complained about are books that need to be downloaded before reading and some pdfs are blank once opened. The value of this research is getting an insight into the behaviour of users towards using apps on different platforms (Android vs iOS) and provides valuable results for the app developers in monitoring usage and enhancing features for the satisfaction of users. The findings reveal that stakeholders/developers need to pay more attention to make the app more user-friendly.

The analytics are performed by using AppBot (appbot.co) and AppFollow (appfollow.io) analytics software's which captured, monitored, measured and analysed the review results for a particular period. These software's provides easy-to-understand insights into an app using artificial intelligence algorithm tools and also provides a large number of data-mining and sentiment analysis features in categories such as Reviews, Sentiment, Words, Phrases, Topics, and Languages. Data Statistics from software's is collected till 7th Sept 2021. Therefore, all the reviews data until that date were included in our dataset.

Country
United States
Keywords

020, mobile phone, national digital library of India (NDLI), Computer Sciences, Apple iOS, deep learning, national digital library of India (NDLI, Education, 004, AppBot, user rating, Android Google play store, Sentiment Analysis, opinion mining, natural language processing (NLP), AppFollow, smart phone, Library and Information Science

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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