E-commerce online review for detecting influencing factors users perception

To date, online shopping using e-commerce services becomes a trend. The emergence of e-commerce truly helps people to shop more effectively and efficiently. However, there are still some problems encountered in e-commerce, especially from the user perspective. This research aims to explore user review data, particularly on factors that influence user perception of e-commerce applications, classify, and identify potential solutions to finding problems in e-commerce applications. Data is grabbed using web scraping techniques and classified using proper machine learning, i.e., support vector machine (SVM). Text associations and fishbone analysis are performed based on the classified user review data. The results of this study show that the user satisfaction problem can be captured. Furthermore, various services that should be provided as a potential solution to experienced customers' problems or application users' perception problems can be generated. A detailed discussion of these findings is available in this article.

A novel user review-based contextual recommender system

Recommendation systems are shrewd applications for knowledge mining that profoundly handle the problem of data overload. Various literature explores different philosophies to create ideas and recommends different strategies according to the needs of customers. Most of the work in the suggested structure space focuses on extending the accuracy of the recommendation by using a few possible methods where the principle purpose remains to improve the accuracy of suggestions while avoiding other plan objectives, such as the particular situation of a client. By using appropriate customer rating data, the biggest test for a suggested system is to generate substantial proposals. A setting is an enormous concept that can think of numerous points of view: for example, the community of friends of a client, time, mindset, environment, organization, type of day, classification of an item, description of the object, place, and language. The rating behavior of customers typically varies in different environments. We have proposed a new review-based contextual recommender (RBCR) system application from this line of analysis, in particular a novel recommender system, which is an adaptable, quick, and accurate piece planning framework that perceives the significance of setting and fuses the logical data using piece stunt while making expectations. We have contrasted our suggested calculation with pre- and post-sifting methods as they have been the most common methodologies in writing to illuminate the issue of setting conscious suggestion. Our studies show that considering the logical data, the display of a system will increase and provide better, appropriate and important results on various evaluation measurements.

To pay or not to pay: understanding mobile game app users' unwillingness to pay for in-app purchases

PurposeWorldwide In-app Purchase (IAP) revenues reached almost US$37 billion in 2017 and doubled that in 2020. Although the revenue from IAPs exceeds those from paid apps, only 5% of total app users make any IAPs. This paper investigates why some users will not make IAPs and develop a novel concept of users' Perceived Aggressive Monetization of IAPs as an alternative framework to explain IAP behavior.Design/methodology/approachGiven the newness of IAPs, this study uses qualitative research to understand the phenomenon and develop a model to explain the decision to spend on IAPs. In total, this study collected 4,092 unique user-generated comments from app user review sites and social media webpages where users discuss in-app purchasing.FindingsThe analysis reveals recurring themes that explain user unwillingness to make in-app purchases, such as conflicting meanings of free-to-play, perceived unfairness and aggressive monetization of IAP by app publishers, and self-control issues. Subsequent user interviews support the themes and suggest that IAP spending might be more impulsive.Originality/valueThe paper develops a new concept of perceived aggressive monetization. Additionally, it proposes a novel theoretical framework that future researchers can use to understand why some mobile game users are unwilling to pay for IAPs.

Sentiment Analysis of Hotel User Review using RNN Algorithm

Sentiment analysis in user review is a growing research area at the current time. Usually, the website becomes a source of data in knowing the quality of the hotel services, and the provider can utilize the review for monitoring and evaluation. However, determining the positive or negative sentiment of a user review in unstructured textual data takes a long time. As a result, we present a model to classify positive or negative sentiment in user reviews in this article. This study suggests the RNN method in building an effective model to classify user sentiment. Based on the experiment, our model can produce accurate results in organizing hotel reviews. Furthermore, the proposed method achieved a higher evaluation metrics score with an f1-score of 91.0%.

Modelling sentiments based on objectivity and subjectivity with self-attention mechanisms

Background: The proliferation of digital commerce has allowed merchants to reach out to a wider customer base, prompting a study of customer reviews to gauge service and product quality through sentiment analysis. Sentiment analysis can be enhanced through subjectivity and objectivity classification with attention mechanisms. Methods: This research includes input corpora of contrasting levels of subjectivity and objectivity from different databases to perform sentiment analysis on user reviews, incorporating attention mechanisms at the aspect level. Three large corpora are chosen as the subjectivity and objectivity datasets, the Shopee user review dataset (ShopeeRD) for subjectivity, together with the Wikipedia English dataset (Wiki-en) and Internet Movie Database (IMDb) for objectivity. Word embeddings are created using Word2Vec with Skip-Gram. Then, a bidirectional LSTM with an attention layer (LSTM-ATT) imposed on word vectors. The performance of the model is evaluated and benchmarked against classification models of Logistics Regression (LR) and Linear SVC (L-SVC). Three models are trained with subjectivity (70% of ShopeeRD) and the objectivity (Wiki-en) embeddings, with ten-fold cross-validation. Next, the three models are evaluated against two datasets (IMDb and 20% of ShopeeRD). The experiments are based on benchmark comparisons, embedding comparison and model comparison with 70-10-20 train-validation-test splits. Data augmentation using AUG-BERT is performed and selected models incorporating AUG-BERT, are compared. Results: L-SVC scored the highest accuracy with 56.9% for objective embeddings (Wiki-en) while the LSTM-ATT scored 69.0% on subjective embeddings (ShopeeRD). Improved performances were observed with data augmentation using AUG-BERT, where the LSTM-ATT+AUG-BERT model scored the highest accuracy at 60.0% for objective embeddings and 70.0% for subjective embeddings, compared to 57% (objective) and 69% (subjective) for L-SVC+AUG-BERT, and 56% (objective) and 68% (subjective) for L-SVC. Conclusions: Utilizing attention layers with subjectivity and objectivity notions has shown improvement to the accuracy of sentiment analysis models.

Analytics for measuring library use and satisfaction of mobile apps

Purpose User review is a significant component of mobile app markets such as the Google Play Store, App Store, Microsoft Store and others. Users submit their reviews for downloaded apps on these sites in the form of star ratings and text reviews. Apps can contain huge volumes of feedback, making it difficult for the user and the developer to skim through thousands of such reviews to get an insight into usage and impact of such apps. Thus, the current study aims to assess the usage and satisfaction among users of the Mendeley’s Android app vs iOS app. Design/methodology/approach The analytics are performed by using Appbot analytics software which captured, monitored, measured and analyzed the review results for a particular period. Appbot provides easy-to-understand insights of an app using artificial intelligence algorithm tools. Findings The findings of the study reveal strong inclination, adoption and usage of Mendeley’s Android app compared to that of iOS among users. Originality/value The value of this research is in getting an insight of the pattern/behavior 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. Without mobile app analytics, one will be blindly trying out different things without any evidence to back up their experiments.

Result Analysis of User Review for Sentiment Classification

Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has been a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods also as well as some existing fuzzy approaches. Afterword comparing the performance of proposed approach with commonly used sentiment classifiers which are known to perform well in this task. The experimental results indicate that our modified approach performs marginally better than the other algorithms.