文章主要以微博"滴滴溫州女孩遇害"話題評論內容為數據源,計算評論內容的情感值,標注情感正負性,通過Word2Vec和SVM方法構建情感分類模型。采用Word2Vec方法計算與此輿情事件中相關的5類主體對象(滴滴、司機、客服、女孩、警察)高相似度的詞語,從情感時序分析和輿情主體對象情感演化分析兩個方面探討微博輿情的情感走勢。通過分析發現,情感分類模型可以有效預測網民的情感走勢;網民的情感時序變化與輿情演變規律相吻合;Word2Vec詞相似度計算模型可以有效反映網民對五類主體對象的情感態度和該輿情階段內的主題特征。

This paper mainly uses the microblog "Didi Wenzhou Girl Killed" topic as the data source,calculates the emotional value of comments,labels the positive and negative emotions,constructs the emotion classification model through Word2 Vec and SVM,and uses the Word2 Vec method to calculate the words with high similarity to the five objects(didi,drivers,customer service,girls,police) involved in this event,and discuss the emotional trend from two aspects:emotional time series analysis and emotional evolution analysis about public opinion objects.It is found that the sentiment classification model can effectively predict the emotional trends of netizens;the change of the time series of netizens’ emotions coincides with the evolution of public opinion;the Word2 Vec word similarity calculation model can effectively reflect the emotional attitude of netizens to five objects and topical features in the stage of public opinion.