Internet Forum Texual Information Filtering Mechanism for Emergency based on Bi-LSTM Neural Network
| Autor: | Songyao Lian, Huiru Cao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Data processing Artificial neural network Computer science business.industry Mechanism (biology) media_common.quotation_subject 05 social sciences Information Dissemination 02 engineering and technology Public opinion Classified information World Wide Web 020901 industrial engineering & automation 0502 economics and business 050211 marketing The Internet Quality (business) business media_common |
| Zdroj: | 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). |
| DOI: | 10.1109/itaic49862.2020.9339174 |
| Popis: | The internet forums are an important way of emergency information dissemination. However, with the development of internet and the vast amounts of users, there is plenty of useless information in internet forum data, especially during emergency. This not only increases the load for the subsequent data processing, but also influences the efficiency and accuracy of public opinion analysis. As a result, the useless data is harmful for the internet public opinion guidance for emergencies. Therefore, in view of internet forum text data of emergency events, in this work, we propose an information filtering mechanism of internet forum based on bilayer long short-term memory (Bi-LSTM) neural network. First, we propose a text information classification model based on LSTM neural network and related mechanism. Then, we evaluate the proposed mechanism by using the practical internet forums. The experimental results and analysis reveal that the proposed algorithm significantly improves the textual information quality as well as the accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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