scholarly journals Analisis Sentimen Dan Topic Modelling Dalam Aplikasi Ruangguru

2020 ◽  
Vol 4 (1) ◽  
pp. 66
Author(s):  
Muhammad Romy Firdaus ◽  
Fikri Muhammad Rizki ◽  
Favian Muhammad Gaus ◽  
Indra Kusumajati Susanto
Keyword(s):  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Topic Modelling ◽  
Educational Content ◽  
Learning Space ◽  
Level Of Education ◽  
Important Thing ◽  
Online Tutoring ◽  
Analysis Methods

This study aims to determine and analyze responses regarding customer satisfaction Ruangguru Application to the learning space features in the Ruangguru Application at every level of education. This is useful to know the strengths and weaknesses of the Ruangguru Application based on sentiment responses from Ruangguru users. Ruangguru is an online tutoring startup and a technology-based educational content service and provider no. 1 in Indonesia. So of course, customer satisfaction is an important thing that is the goal of the company. So that when customer satisfaction is met, that is where the company can realize their goals. To see how the level of customer satisfaction, sentiment analysis methods and topic modeling are used in processing the data so that responses can be seen as to what is provided by the customer so that it can be an evaluation for the Ruangguru Application.

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

Analyzing Brexit’s impact using sentiment analysis and topic modeling on Twitter discussion

2020 ◽  
Author(s):  
Sardar Haider Waseem Ilyas ◽  
Zainab Tariq Soomro ◽  
Ahmed Anwar ◽  
Hamza Shahzad ◽  
Ussama Yaqub
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling

On the combination of "off-the-shelf" sentiment analysis methods

2016 ◽  
Author(s):  
Pollyanna Gonçalves ◽  
Daniel Hasan Dalip ◽  
Helen Costa ◽  
Marcos André Gonçalves ◽  
Fabrício Benevenuto
Keyword(s):  
Sentiment Analysis ◽  
Analysis Methods

scholarly journals Understanding the nature and scope of clinical research commentaries in PubMed

2019 ◽  
Vol 27 (3) ◽  
pp. 449-456
Author(s):  
James R Rogers ◽  
Hollis Mills ◽  
Lisa V Grossman ◽  
Andrew Goldstein ◽  
Chunhua Weng
Keyword(s):  
Clinical Research ◽  
Sentiment Analysis ◽  
Random Sample ◽  
Full Text ◽  
Topic Modeling ◽  
Critical Appraisal ◽  
Research Articles ◽  
Disease Specific ◽  
Research Studies

Abstract Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata. Five percent of clinical research studies (N = 130 629) received postpublication comments (N = 171 556), resulting in 178 882 comment–article pairings, with 90% published in the same journal. We obtained 5197 full-text comments for topic modeling and exploratory sentiment analysis. Topics were generally disease specific with only a few topics relevant to the appraisal of studies, which were highly prevalent in letters. Of a random sample of 518 full-text comments, 67% had a supportive tone. Based on our results, published commentary, with the exception of letters, most often highlight or endorse previous publications rather than serve as a prominent mechanism for critical appraisal.

scholarly journals Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Andrew J Reagan ◽  
Christopher M Danforth ◽  
Brian Tivnan ◽  
Jake Ryland Williams ◽  
Peter Sheridan Dodds
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Analysis Methods

Covid-19 Vaccine Tweets Sentiment Analysis and Topic Modelling for Public Opinion Mining

2021 ◽  
Author(s):  
Trisha Baldha ◽  
Malvi Mungalpara ◽  
Priyanka Goradia ◽  
Santosh Bharti
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Topic Modelling

Demographic Influences on E-Payment Services

2017 ◽  
Vol 13 (1) ◽  
pp. 44-65 ◽  
Author(s):  
Winfred Yaokumah ◽  
Peace Kumah ◽  
Eric Saviour Aryee Okai
Keyword(s):  
Customer Satisfaction ◽  
Analysis Of Variance ◽  
Ict Literacy ◽  
Payment Systems ◽  
Ict Skills ◽  
Male And Female ◽  
Level Of Education ◽  
Payment Services ◽  
Survey Responses ◽  
Perception Of Security

This study investigated customers' preferences of payment systems and the influence of demography on the attitude of customers towards e-payment services. Survey responses were received from 558 bank customers. The t-test and the analysis of variance were employed to examine the differences in perception of security, ICT literacy, customer satisfaction, and the use of e-payment services based on customers' age, gender, and the level of education. The findings revealed no significant differences between the male and female customers in the use of e-payment services. However, the male customers had higher ICT skills, yet perceived e-payment services less secured. Moreover, there were no significant differences in satisfaction and e-payment use, though, customers with higher level of education felt less secured using the services. Also, whereas the older customers were more satisfied with e-payment services, the younger customers had more ICT skills and use the services much more. These findings are necessary for formulating strategies for marketing e-payment services.

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