scholarly journals Sentiment Analysis of Pharmaceutical Products Evaluation Based on Customer Review Mining

2018 ◽  
Vol 11 (3) ◽  
Author(s):  
Mahboob K ◽  
Hina S
Keyword(s):  
Sentiment Analysis ◽  
Pharmaceutical Products ◽  
Review Mining ◽  
Customer Review

scholarly journals CUSTOMER REVIEW ANALYSIS - MULTI-LABEL CLASSIFICATION AND SENTIMENT ANALYSIS

2020 ◽  
Vol 07 (04) ◽  
pp. 28-32
Author(s):  
Shreyas Renga ◽  
Abishek Ganapathy ◽  
T. Hasith Ram Varma
Keyword(s):  
Sentiment Analysis ◽  
Review Analysis ◽  
Customer Review

scholarly journals Symptoms to Disease Mapping and Doctor Recommendation System

2019 ◽  
Vol 9 (1) ◽  
pp. 1111-1119
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Disease Mapping ◽  
Uneven Distribution ◽  
Bayes Classifier ◽  
Doctor Who ◽  
Mining Technique ◽  
Review Mining ◽  
Doctor Recommendation

To find an appropriate doctor who is specialized to treat a certain disease while only symptoms are known is not easy job for the patients. In this paper, we describe a recommended framework to find the best doctors in accordance with patients' requirements. In the proposed system, first it considers only those doctors whose profile match with patients' requirements. Second, the best doctors will be recommended out of previously obtained doctors based on the parameter patients' feedback i.e., patients' review. Our proposal will suggest a doctor recommendation system that uses review mining technique, which can be used in those countries that have huge uneven distribution of medical resources. In our model we have used the decision tree for symptoms to disease mapping and Naive Bayes classifier for sentiment analysis which are connected to each other using a bridge of python logic and the required output is top doctors based on the users input

scholarly journals Measuring e-Commerce service quality from online customer review using sentiment analysis

2018 ◽  
Vol 971 ◽  
pp. 012053 ◽  
Author(s):  
Puspita Kencana Sari ◽  
Andry Alamsyah ◽  
Sulistyo Wibowo
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Customer Review

A Role-based Customer review Mining System

2006 ◽  
Author(s):  
Wenqian Shang ◽  
Youli Qu ◽  
Houkuan Huang ◽  
Yongmin Lin ◽  
Hongbin Dong
Keyword(s):  
Mining System ◽  
Review Mining ◽  
Customer Review ◽  
Role Based

scholarly journals Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Alifia Revan Prananda ◽  
Irfandy Thalib
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Business Intelligence ◽  
Ground Truth ◽  
Classification Model ◽  
Support Vector ◽  
Intelligence Analysis ◽  
Market Demand ◽  
Customer Review ◽  
Negative Sentiment

Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion:  According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows      that the decision tree provides the best performance.

scholarly journals Sentiment Analysis of Smartphone Accounting Application Users

2021 ◽  
Vol 6 (2) ◽  
pp. 161-174
Author(s):  
Kholifah Fil Ardhi
Keyword(s):  
Qualitative Research ◽  
Sentiment Analysis ◽  
Mining Method ◽  
Financial Reports ◽  
Review Mining ◽  
Product Features ◽  
Feature Based ◽  
Small Medium Enterprises ◽  
Medium Enterprises ◽  
Practical Recommendations

The focus of this research is to summarize the reviews conducted by accounting application users to explore what aspects they like about the accounting application. This research uses review sentences with a total of 4923 review sentences on Google and Apple platforms. The review mining method used in this study implements the Feature-Based Summarization (FBS). The conclusion of this study is that there are six product features that are preferred by accounting application users. The product features are reports, transactions, bookkeeping, profit, category, and customers. This research has explored product features in accounting applications, but not all product features are discussed by users. Therefore, the discussion on review sentences focuses on the six product features. This study is able to provide practical recommendations to Small-Medium Enterprises (SMEs) actors in making smartphone-based application decisions they will use. This study recommends SMEs to use accounting applications with the above product features. This is because the strong discussion of opinions on product features explains the preference for product features for actors in helping them prepare financial reports. As qualitative research, this study does not have the ability to generalize the results of the study to a population.

Sign in / Sign up

Export Citation Format

Share Document