Abstract
In order to timely and accurately analyze the focus and appeal of public opinion on the Internet, A LSTM-ATTN model was proposed to extract the hot topics and predict their changing trend based on tens of thousands of news and commentary messages. First, an improved LDA model was used to extract hot words and classify the hot topics. Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism Long Short-Term Memory (LSTM) network, which named LSTM-ATTN model. A large number of numerical experiments were carried out using the public opinion information of "African classical swine fever" event in China. According to results of evaluation indexes, the relative superiority of LSTM-ATTN model was demonstrated. It can capture and reflect the inherent characteristics and periodic fluctuations of the agricultural public opinion information. Also, it has higher convergence efficiency and prediction accuracy.
Highlights
With the rapid development of social informatization, a variety of news, information and comment information have been rapidly and widely disseminated on the Internet
Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism long short-term memory (LSTM) network, which named LSTM-ATTN model
Based on the above analysis of the current research literature, this paper proposes a prediction model LSTM-ATTN based on attention mechanism to extract hot topics and predict heat change of agricultural public opinion
Summary
With the rapid development of social informatization, a variety of news, information and comment information have been rapidly and widely disseminated on the Internet. Different from other types of public opinion, agricultural public opinion is often closely related to the safety of food and supplies in people ‘ s daily life, which is more likely to cause widespread and sustained attention, and even lead to the agglomeration effect of negative emotions such as complaints and panic. This maybe has a negative impact on agricultural industry development and social stability. It is necessary to improve the monitoring ability of agricultural public opinion, quickly and accurately understand the hot topics of public opinion. By analyzing the hot topics and emotional attitudes of the people in public opinion, the government can guide public opinion effectively and prevent the spread of negative emotions and false public opinion
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