Search

Your search keyword '"Mao, Dianhui"' showing total 40 results
Start Over Author "Mao, Dianhui"
40 results on '"Mao, Dianhui"'

12. Bike-Sharing Prediction System

Author

Cai, Qiang, Xue, Ziyu, Mao, Dianhui, Li, Haisheng, Cao, Jian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Josef, Kittler, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, El Rhalibi, Abdennour, editor, Tian, Feng, editor, Pan, Zhigeng, editor, and Liu, Baoquan, editor

Published
2016
Full Text
View/download PDF

22. A prediction model of micro-blog affective hotspots based on SVM collaborative filtering recommendation model

Author

Mao Dianhui and Song Zihao

Subjects
Microblogging, Computer science, Event (computing), business.industry, 020206 networking & telecommunications, 02 engineering and technology, Predicate (mathematical logic), Public life, Machine learning, computer.software_genre, Computer Graphics and Computer-Aided Design, Computer Science Applications, Recommendation model, Support vector machine, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Social media, Artificial intelligence, business, computer, Software
Abstract

As innumerable events are reported on Weibo every day, it becomes especially important to predicate the event development trends early as possible. Weibo has been indispensable to the public life; ...

Published
2018
Full Text
View/download PDF

25. Sentiment Analysis of Review Data Using Blockchain and LSTM to Improve Regulation for a Sustainable Market.

Author

Zhao, Zhihua, Hao, Zhihao, Wang, Guancheng, Mao, Dianhui, Zhang, Bob, Zuo, Min, Yen, Jerome, and Tu, Guangjian

Subjects
SENTIMENT analysis, BLOCKCHAINS, DATA analysis, DATA warehousing, TREND analysis, DEEP learning, FEATURE extraction
Abstract

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users' emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results