A study on support method of consulting service using text mining-Application to real problem-

2019 ◽  
Author(s):  
Ruriko Watanabe ◽  
Nobutada Fujii ◽  
Daisuke Kokuryo ◽  
Toshiya Kaihara ◽  
Yoichi Abe ◽  
...  
Keyword(s):  
Text Mining ◽  
Real Problem ◽  
Text Mining Application

scholarly journals Customer Sentiments towards Fin-Tech Apps in India: A Text Mining Application

2020 ◽  
Vol 9 (1) ◽  
pp. 384-387
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Service Providers ◽  
Online Reviews ◽  
Customer Experience ◽  
Internet Connectivity ◽  
The Government ◽  
Text Mining Application

Fintech sector has witnessed incredible growth in India with the government promoting digital and cashless transactions along with the penetration of smartphones and internet connectivity in the country. Online reviews and customer opinions play a key role in the choice of Fintech apps among users. The customers compare the services of these service providers based on online reviews and ratings to finalize their choice. Fintech companies use this data to improve their customer experience. In this paper we attempt to provide useful insights into the customer sentiments towards Fintech apps in India by using a text mining approach. By analyzing customer opinions and reviews, we attempt to understand the acceptance of the services provided by the Fintech companies in India. We have used sentiment analysis to classify positive, negative and neutral reviews to understand the user sentiment towards Fintech apps. We also put forward suggestions to address the gaps in the services provided by these Fintech companies based on our analysis and findings.

scholarly journals The evolution of knowledge on genes associated with human diseases

2021 ◽  
Author(s):  
Thomaz Luscher-Dias ◽  
Rodrigo JS Dalmolin ◽  
Paulo P Amaral ◽  
Tiago L Alves ◽  
Viviane Schuch ◽  
...  
Keyword(s):  
Text Mining ◽  
Target Genes ◽  
Machine Learning Algorithms ◽  
Human Diseases ◽  
Knowledge Network ◽  
Biological Information ◽  
Cognitive Computing ◽  
Therapeutic Drugs ◽  
Molecular Bases ◽  
Text Mining Application

Thousands of scientific articles describing genes associated with human diseases are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprised of 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our approach helped to unravel the molecular bases of diseases over time, and to detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes which are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating in this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.

scholarly journals Linking Twitter Sentiment Knowledge with Infrastructure Development

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Zakya Reyhana ◽  
Kartika Fithriasari ◽  
Moh. Atok ◽  
Nur Iriawan
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Automatic Extraction ◽  
Infrastructure Development ◽  
Classification Methods ◽  
Negative Comments ◽  
Text Mining Application ◽  
Better Than

Sentiment analysis is related to the automatic extraction of positive or negative opinions from the text. It is a special text mining application. It is important to classify implicit contents from citizen’s tweet using sentiment analysis. This research aimed to find out the opinion of infrastructure that sustained urban development in Surabaya, Indonesia’s second largest city. The procedures of text mining analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT), username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified into positive and negative comments. Classification methods used in this research were support vector machine (SVM) and neural network (NN). The result of this research showed that NN classification method was better than SVM.

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