scholarly journals Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

2021 ◽  
Vol 20 (1) ◽  
pp. 33-42
Author(s):  
Dr. M. Deva Priya ◽  
◽  
R. Rithika ◽  
Keyword(s):  
Sentiment Analysis ◽  
User Reviews

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Sentiment Analysis using Deep Belief Network for User Rating Classification

2021 ◽  
Vol 10 (8) ◽  
pp. 87-91
Author(s):  
Ravi Chandra ◽  
Basavaraj Vaddatti
Keyword(s):  
Sentiment Analysis ◽  
Feature Vector ◽  
Deep Belief Network ◽  
Deep Belief Networks ◽  
User Reviews ◽  
Belief Network ◽  
Precision And Accuracy ◽  
People’S Attitudes ◽  
User Assessment ◽  
Different Levels

People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.

Detecting Usability and User Experience Issues in Stroke Caregiving Apps: An Analysis of User Reviews (Preprint)

2021 ◽  
Author(s):  
Elton Lobo ◽  
Mohamed Abdelrazek ◽  
Anne Frølich ◽  
Lene Juel Rasmussen ◽  
Patricia M. Livingston ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
User Experience ◽  
Participatory Design ◽  
User Participation ◽  
User Reviews ◽  
App Store ◽  
Healthcare Needs ◽  
Recovery Trajectory ◽  
Mhealth Apps

BACKGROUND Stroke caregivers often experience negative impacts when caring for a person living with a stroke. Technologically based interventions such as mHealth apps have demonstrated potential in supporting the caregivers during the recovery trajectory. Hence, there is an increase in apps in popular app stores, with a few apps addressing the healthcare needs of stroke caregivers. Since most of these apps were published without explanation of their design and evaluation processes, it is necessary to identify the usability and user experience issues to help app developers and researchers to understand the factors that affect long-term adherence and usage in stroke caregiving technology. OBJECTIVE The purpose of this study was to determine the usability and user experience issues in commercially available mHealth apps from the user reviews published within the app store to help researchers and developers understand the factors that may affect long-term adherence and usage. METHODS User reviews were extracted from the previously identified 47 apps that support stroke caregiving needs using a python-scraper for both app stores (i.e. Google Play Store and Apple App Store). The reviews were pre-processed to (i) clean the dataset and ensure unicode normalization, (ii) remove stop words and (iii) group words together with similar meanings. The pre-processed reviews were filtered using sentiment analysis to exclude positive and non-English reviews. The final corpus was classified based on usability and user experience dimensions to highlight issues within the app. RESULTS Of 1,385,337 user reviews, only 162,095 were extracted due to the limitations in the app store. After filtration based on the sentiment analysis, 15,818 reviews were included in the study and were filtered based on the usability and user experience dimensions. Findings from the usability and user experience dimensions highlight critical errors/effectiveness, efficiency and support that contribute to decreased satisfaction, affect and emotion and frustration in using the app. CONCLUSIONS Commercially available mHealth apps consist of several usability and user experience issues due to their inability to understand the methods to address the healthcare needs of the caregivers. App developers need to consider participatory design approaches to promote user participation in design. This might ensure better understanding of the user needs and methods to support these needs; therefore, limiting any issues and ensuring continued use.

scholarly journals Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 473
Author(s):  
Yongpeng Wang ◽  
Hong Yu ◽  
Guoyin Wang ◽  
Yongfang Xie
Keyword(s):  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Mapping Function ◽  
Linear Mapping ◽  
Feature Mapping ◽  
Target Domain ◽  
User Reviews ◽  
Cross Domain ◽  
Semantic Orientation ◽  
Label Information

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

scholarly journals Identification of Fraud Apps Using Sentiment Analysis Techniques

2021 ◽  
Vol 15 (23) ◽  
pp. 178-185
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Sentiment Analysis ◽  
User Feedback ◽  
Sensitive Data ◽  
User Reviews ◽  
Analysis Techniques ◽  
Android App ◽  
Fake Reviews ◽  
Google Play

Considering that application’s security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to spot insecure apps by analyzing user feedback on Google Play platform using sentiment analysis. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application. But unfortunately, not all of these reviews are real, fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews and deliver accurate and correct results. This tool is useful for both users wanting to install an android app and for developers interested in app’s optimization.

scholarly journals Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain

2020 ◽  
Vol 7 (3) ◽  
pp. 599
Author(s):  
Arif Bijaksana Putra Negara ◽  
Hafiz Muhardi ◽  
Indira Melinda Putri
Keyword(s):  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Review Section ◽  
User Reviews ◽  
Average Value

<p class="Abstrak">Zaman sekarang tren masyarakat untuk memesan tiket pesawat sudah melalui situs-situs <em>booking</em> <em>online</em>. Pegipegi.com merupakan salah satu <em>website</em> yang menyediakan pemesanan tiket dan menyediakan fitur ulasan bagi pengunjung untuk menyampaikan opini. Pengunjung lain yang membaca ulasan-ulasan tersebut dapat memperoleh gambaran secara lebih objektif mengenai maskapai penerbangan. Ulasan pengguna yang terdapat pada website pegipegi.com saat ini sudah sangat banyak sehingga hal ini menyulitkan dan memakan waktu untuk membaca secara keseluruhan. Oleh karena itu dirancang analisis sentimen guna membantu mengklasifikasi ulasan kedalam kategori positif atau negatif sehingga dapat memberikan rekomendasi maskapai penerbangan berdasarkan jumlah kategori ulasan. Metode yang diterapkan untuk klasifikasi sentimen adalah Naïve Bayes dengan seleksi fitur <em>Information Gain</em>. Adapun tujuan dari penelitian ini adalah mengetahui pengaruh dari pemilihan fitur <em>Information Gain</em> terhadap akurasi klasifikasi dan membuktikan bahwa metode Naïve Bayes dengan <em>Information Gain</em> dapat digunakan untuk klasifikasi analisis sentimen. Hasil pengujian yang telah dilakukan menunjukkan bahwa nilai rata-rata akurasi, <em>precision</em>, <em>recall</em> setelah penambahan <em>Information Gain</em> menunjukkan hasil yang lebih baik sebesar 0,865 jika dibandingkan sebelum penambahan information gain yakni sebesar 0,81.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Nowadays people tend to order airplane tickets through online booking sites. Pegipegi.com is a website that provides ticket reservations and a review section for visitors to express their opinions. Other visitors who read the reviews can get a more objective picture of airlines. The user reviews contained on the pegipegi.com website are currently very large so this makes it difficult and time consuming to read in its entirety. Therefore sentiment analysis is designed to help classify reviews into positive or negative categories so that they can provide airline recommendations based on the number of review categories. The method applied for sentiment classification is Naïve Bayes with the Information Gain feature selection. The purpose of this study was to determine the effect of selecting the Information Gain feature on classification accuracy and prove that the Naïve Bayes method with Information Gain can be used for the classification of sentiment analysis. The results of the tests that have been done show that the average value of accuracy, precision, recall after adding Information Gain shows better results of 0.865 compared to the addition of information gain which is equal to 0.81</em>.</em></p>

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