Enhancing Traffic Congestion Estimation with Social Media by Coupled Hidden Markov Model

2016 ◽  
pp. 247-264 ◽  
Author(s):  
Senzhang Wang ◽  
Fengxiang Li ◽  
Leon Stenneth ◽  
Philip S. Yu
Keyword(s):  
Social Media ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Traffic Congestion ◽  
Hidden Markov

Hidden Markov Model for Sentiment Analysis using Viterbi Algorithm

2021 ◽  
Vol 2 (1) ◽  
pp. 18-23
Author(s):  
Nursyiva Irsalinda ◽  
Haswat Haswat ◽  
Sugiyarto Sugiyarto ◽  
Meita Fitrianawati
Keyword(s):  
Social Media ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Text Data ◽  
Analysis Process ◽  
The People ◽  
Text Data Analysis ◽  
Almost All

Data mining is an activity to extract the knowledge from large amounts of data as very important information. The type of data in the era of 4.0 is data in the form of text, which is very much derived from social media. Recently, text becomes very important in some applications, such as the processing and the conclusion of a person's review and analysis of political opinion which is very sensitive in almost all countries, including Indonesia. Online text data that circulating on social media has several shortcomings that could potentially hinder the analysis process. One of the drawbacks is the people can post their own content freely, so the quality of their opinions cannot be guaranteed such as spam and irrelevant opinions. The other drawback is the basic truth of the online text data is not always available. Basic truth is more like a particular opinion, indicating whether the opinion is positive, negative and neutral. Therefore, the main objective of this study is to improve the forecasting accuracy of online text data analysis from social media. The method used os Hidden Markov Model (HMM) with Viterbi Algorithm that applied to extract the dataset sentiment at the 2015 elections in Surabaya from the popular site micro blogging called Twitter. The result of the study is Viterbi algorithm has predicted the best route with the candidate Tri Rismaharini gained a prediction of neutral sentiments, whereas ratio candidates gained sentiment negative predictions as well. The proposed Model is accurate to predict candidate features. It also helps political parties to introduce candidates based on reviews so that they can increase candidate performance or they can manage broad publicity to promote candidates.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
Sign in / Sign up

Export Citation Format

Share Document