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

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

ANALISIS SENTIMEN ULASAN APLIKASI LINKEDIN DALAM GOOGLE PLAY STORE DENGAN MODEL NAÏVE BAYES

In the era of digitalization, mobile applications have become a basic necessity in individual lives, especially in the work context. LinkedIn, as a mobile application that focuses on providing job and recruitment information. With the significant growth of LinkedIn users in 2023 with 930 registered users, many users will provide reviews on the Google Play Store regarding their experiences. The aim of this research is to collect information on reviews on the Google Play Store regarding the LinkedIn application service through sentiment analysis using the data mining classification method with the Naïve Bayes model. Through previous research, this method is considered better than other classification methods. After applying this model through testing on test data, test results were obtained which showed that the majority of LinkedIn application reviews had negative sentiment with an accuracy value of 84% which was presented in bar charts and wordcloud

Towards explaining user satisfaction with contact tracing mobile applications in a time of pandemic: a text analytics approach

This research project investigates the critical phenomenon of the post-adoption use of Contact Tracing Mobile Applications (CTMAs) in a time of pandemic. A panel data set of customer reviews was collected from March 2020 to June 2021. Using sentiment analysis, topic modeling and dictionary-based analytics, 10,337 reviews were analyzed. The results show that after controlling for review sentiment and length, user satisfaction is associated with users’ perception of utilitarian benefits of CTMA, their CTMA-specific privacy concerns, and installation and use issues. Our methodological approach (using various text analysis techniques for analyzing public feedback) and findings (influential factors on consumers’ satisfaction with CTMA) can inform the design and deployment of the next generation of CTMAs for managing future pandemics

TWEEZER – Tweets Analysis

Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm –Naïve Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naïve bayes means algorithm with Naïve bayes classification

Understanding user behavior aspects on emergency mobile applications during emergency communications using NLP and text mining techniques

Abstract. The use of mobile devices has been skyrocketing in our society. Users can access and share any type of information in a timely manner through these devices using different social media applications. This enabled users to increase their awareness of ongoing events such as election campaigns, sports updates, movie releases, disaster occurrences, and studies. The attractiveness, affordability, and two-way communication capabilities empowered these mobile devices that support various social media platforms to be central to emergency communication as well. This makes a mobile-based emergency application an attractive communication tool during emergencies. The emergence of mobile-based emergency communication has intrigued us to learn about the user behavior related to the usage of these applications. Our study was mainly conducted on emergency apps in Nordic countries such as Finland, Sweden, and Norway. To understand the user objects regarding the usage of emergency mobile applications we leveraged various Natural Language Processing and Text Mining techniques. VADER sentiment tool was used to predict and track users’ review polarity of a particular application over time. Lately, to identify factors that affect users’ sentiments, we employed topic modeling techniques such as the Latent Dirichlet Allocation (LDA) model. This model identifies various themes discussed in the user reviews and the result of each theme will be represented by the weighted sum of words in the corpus. Even though LDA succeeds in highlighting the user-related factors, it fails to identify the aspects of the user, and the topic definition from the LDA model is vague. Hence we leveraged Aspect Based Sentiment Analysis (ABSA) methods to extract the user aspects from the user reviews. To perform this task we consider fine-tuning DeBERTa (a variant of the BERT model). BERT is a Bidirectional Encoder Representation of transformer architecture which allows the model to learn the context in the text. Following this, we performed a sentence pair sentiment classification task using different variants of BERT. Later, we dwell on different sentiments to highlight the factors and the categories that impact user behavior most by leveraging the Empath categorization technique. Finally, we construct a word association by considering different Ontological vocabularies related to mobile applications and emergency response and management systems. The insights from the study can be used to identify the user aspect terms, predict the sentiment of the aspect term in the review provided, and find how the aspect term impacts the user perspective on the usage of mobile emergency applications

Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF

AicoGPT (Generative Pre-trained Transformer) Sentiment Analysis Using TFIDF. The role of artificial intellegence makes it easy to find precise and accurate information, and even solve complex problem models. One of the AI-based breakthroughs is ChatGPT by OpenAI in 2020, followed by the latest version in 2023, GPT-3. Since, several AI technologies similar to mobile versions have emerged, one of which’s AicoGPT. However, the performance of similar applications cannot be relied on, so it’s still necessary to analyze its users' responses, whether they’ll be as amazing or not. So, from these problems, this research aims to analyse 1443 reviews from users of the AicoGPT application on Google Playstore using sentiment analysis techniques using TF-IDF and a comparison of LR and SVM classifications. Of the two trials, producing the best accuracy with SVM, which’s equal to 92%. While LR produces an accuracy of 89%. From this study, it can be concluded briefly that TF-IDF with SVM classification’s suitable for carrying out a sentiment analysis of the dataset

Designing positive behavior change experiences: a systematic review and sentiment analysis based on online user reviews of fitness and nutrition mobile applications

While mobile devices have become ubiquitous, illnesses derived from poor lifestyle habits are on the rise. However, our understand ing of design mechanisms that induce healthier behavior change through mobile devices is still limited. Using the BCT Taxonomy, and online user reviews as an indicator of experience satisfaction, we make a three-folded contribution to designing interactive sys tems for behavior change: (i) a systematic review of applications for physical activity and healthier eating habits, coding BCTs; (ii) sentiment analysis performed on 20492 review sentences of these apps; and (iii) design implications regarding the implementation features for each BCT cluster, considering the highest-scored fea tures in terms of sentiment analysis. Positive expressions referred to the framing/reframing technique. Contrarily, negative expres sions were mostly related to reward and threat. Findings from this study can be used to benchmark interactions between users and behavior change interfaces, and provide design insights to support positive user experiences.info:eu-repo/semantics/publishedVersio

Assessing the effect of mobile word-of-mouth on consumers : the physical, psychological and social influences

Mobile technologies enable users to discover and research products anytime, anywhere. Mobile devices allow consumers to create and share content based on physical location, facilitate seamless interactions, and provide context-relevant information that can better satisfy users’ needs and enhance their shopping experience. As consumers increasingly rely on mobile devices to search information and purchase products, they need immediate, updated, informative and credible opinions in concise forms. Meanwhile, marketers face unprecedented opportunities for mobile marketing, making ever important for them to understand the mobile word-of-mouth and its effect on the purchase behaviors of consumers on the mobile platform vs. those on other devices. Drawing from the media richness theory and the principle of compensatory adaptation, study one performs sentiment analysis of online product reviews from both mobile and desktop devices by analyzing over one million customer reviews from Dianping.com. We find that mobile reviews are naturally shorter, contain more adverbs and adjectives, and have smaller readership and less votes of helpfulness. The product ratings from mobile reviews are more polarized yet the average valence of mobile reviews is higher. By comparison, desktop reviews contain more pictures and are rated more helpful. Lastly, pricy products receive more desktop reviews than mobile ones. Study two draws from the construal level theory and posit that WOM from mobile devices reflects closer psychological distances (temporal and social), thus constitutes a lower construal level than that from desktop computers. Using a dataset of over one million product reviews from Dianping.com, we assess the value of online product reviews from mobile devices in comparison with those from the desktop computers. Our findings show that WOM is more helpful when it is socially and temporally closer to the users and this effect is amplified when using mobile devices, which bring the mental construal to a low level and make others’ opinions more relevant. Further, we show that product type moderates the effect of online reviews in that m-WOM is more influential for hedonic products and its value for the utilitarian consumption is the lowest. Study three deploys the observational learning theory to examine the effect of WOM across the mobile and desktop devices on the purchase behavior of online promotional offers. The findings suggest that the effect of WOM on the purchase of promotion offers varies significantly across the platforms, product categories, and discount rates. These findings help better understand the strengths, limitations and the effect of m-WOM as marketers attempt to offer consumers context-sensitive and time-critical promotions through mobile devices and make a significant contribution to the literature on interactive marketing. These studies render meaningful implications for theory development about the role of mobile technologies in marketing and can assist practitioners formulating effective promotional strategies through the electronic channels via mobile and desktop devices

Aspect-Based Sentiment Analysis on Mobile Game Reviews Using Deep Learning

This paper proposes an aspect-based sentiment analysis method on mobile game reviews using deep learning, which can make better use of massive mobile game reviews data to judge users\u27 emotional tendencies for different attributes of the game at a fine-grained level. Specifically, there are three models in our sentiment analysis method. The baseline model includes Bi-LSTM, FCN, and CRF for sentiment collocation extraction, matching, and classification. The iterative model updates the neural network structure and effectively improves the model\u27s recall rate in the experiments. The joint model is based on the information passing mechanism and further improves the comprehensive performance of the model. We crawled more than 100,000 game review items from two well-known Chinese game review websites Bilibili and Taptap and manually annotated 3,000 items to construct the experiment dataset. Several experiments have been carried out to evaluate our methods. The experimental results show that our methods have achieved good results