Research results of a company specializing in sociological surveys in the internet field show that more than 95% of people access the internet to read information, mainly via news aggregator websites and social networks. Accordingly, the demand for seeking and using information is the fundamental need of internet users.[1] Thus, the extraction and synthesis of public opinion can bring considerable benefits to those who are particularly interested. In order to support efficient and fast extraction and synthesis of public opinion, artificial intelligence, especially machine learning and natural language processing is expected to significantly automate some processes of that work. Extracting information automatically from texts has been become an important research topic of Natural language processing (NLP) for many decades.[2] Some of the main research issues related to the automated analysis of these texts include sentiment analysis (opinion extraction), emotion recognition, and reasoning (emotion recognition). detecting), sarcasm/sarcasm detection, discovery of rumors and authenticity, and detection of fake news. [2] Achieving automated and high-performance solutions to these problems will hold promise for tasks such as trend and market analysis, obtaining user reviews for products, opinion surveys, targeted advertising, polling, predictions for elections and referendums, automatic media monitoring, and filtering out unconfrmed content for better user experience, to online public health surveillance. [2] Stance detection is a significant recent member of an aforementioned research issues group. [2] It is usually considered as a subproblem of sentiment analysis.[2] In this article, we will study a stance detection for Vietnamese using monitoring machine learning method, namely using traditional model (SVM) and deep learning approaches like LSTM, RNN.
Readership Map
Content Distribution
Research results of a company specializing in sociological surveys in the internet field show that more than 95% of people access the internet to read information, mainly via news aggregator websites and social networks. Accordingly, the demand for seeking and using information is the fundamental need of internet users.[1] Thus, the extraction and synthesis of public opinion can bring considerable benefits to those who are particularly interested. In order to support efficient and fast extraction and synthesis of public opinion, artificial intelligence, especially machine learning and natural language processing is expected to significantly automate some processes of that work. Extracting information automatically from texts has been become an important research topic of Natural language processing (NLP) for many decades.[2] Some of the main research issues related to the automated analysis of these texts include sentiment analysis (opinion extraction), emotion recognition, and reasoning (emotion recognition). detecting), sarcasm/sarcasm detection, discovery of rumors and authenticity, and detection of fake news. [2] Achieving automated and high-performance solutions to these problems will hold promise for tasks such as trend and market analysis, obtaining user reviews for products, opinion surveys, targeted advertising, polling, predictions for elections and referendums, automatic media monitoring, and filtering out unconfrmed content for better user experience, to online public health surveillance. [2] Stance detection is a significant recent member of an aforementioned research issues group. [2] It is usually considered as a subproblem of sentiment analysis.[2] In this article, we will study a stance detection for Vietnamese using monitoring machine learning method, namely using traditional model (SVM) and deep learning approaches like LSTM, RNN.
