Research on the emotional polarity of Academic micro-blog knowledge transfer

Abstract

[Purpose / significance] This paper takes the official tweet of the Chinese Academy of Sciences as an example to explore the emotional polarity tendency of academic tweet in knowledge transfer and the relationship between these different emotional tedecies and its influential data, so as to analyze the current situation of academic public opinion, and through the analysis of the relationship between different indicators, to provide reference and suggestions for better transfer of academic knowledge. [Method / process] Based on the extended sentiment dictionary, the sentiment analysis rules are established, and the program language is used to recognize the sentiment score of comments. After getting the total score of the comments below each tweet, the relationship between the sentiment of the tweet and its corresponding influential data is identified by using the correlation analysis method. [Results / Conclusion] First of all, the comments under the tweet published by the Chinese Academy of sciences are mainly positive emotions, and the overall emotions of the tweet are also positive, but there are also some negative public opinions. Secondly, there is a moderate or above positive correlation between the overall emotion of the tweet and its influential data (number of Forwards, number of Comments, number of Likes), and the greater the number of the influential data is, the higher the emotional positivity of the original text will be. Finally, in the group of indicators of Forward--Comment, they always present a high positive correlation, while in the two groups of indicators of Forward--Like and Comment--Like, the difference in the number of samples will have a great impact on the results, and with the increase of the number of samples, the correlation is decreasing.