A public and large-scale expert information fusion method and its application: Mining public opinion via sentiment analysis and measuring public dynamic reliability

2022 ◽  
Vol 78 ◽  
pp. 71-85
Author(s):  
Xiaohong Chen ◽  
Weiwei Zhang ◽  
Xuanhua Xu ◽  
Wenzhi Cao
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Information Fusion ◽  
Large Scale ◽  
Fusion Method ◽  
Dynamic Reliability ◽  
Expert Information

Sentiment Analysis of Public Opinion Related to Rapid Test Using LDA Method

2021 ◽  
Vol 5 (4) ◽  
pp. 672-679
Author(s):  
Viny Gilang Ramadhan ◽  
Yuliant Sibaroni
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Rapid Test ◽  
Test Results ◽  
Indonesian Government ◽  
Large Scale Data ◽  
Rbf Kernel ◽  
Large Scale Data Processing ◽  
Scale Data

In 2020 the world will be shocked by an outbreak of a disease that has developed tremendously. This disease is the Coronavirus. The Indonesian government, in overcoming conducted a Rapid early detection test in the spread of the Coronavirus. The steps of the Indonesian government have received rejection in several areas because people consume hoax news on social media. Indonesians widely use Twitter in conversations about the Coronavirus. Previous research was carried out using large-scale data, which affected the performance of the topic extraction method. The classification used resulted in poor accuracy using LDA to find the probability of topics in existing documents. LDA excels in large-scale data processing and is more consistent in generating the topic proportion value and word probability. Aspect-based sentiment analysis on public opinion regarding the rapid test on Twitter using LDA can determine aspects and public opinion on the rapid test. The test results of this study obtained 7000 tweets, four aspects of the results of topic using LDA, and getting the best accuracy using the RBF kernel by 95%. The sentiment of the Indonesian people towards the Rapid test is positive, with 4,305 sentiments.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data

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
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