The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification re...The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments.Inspired by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon.The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.展开更多
基金the National Natural Science Foundation of China(No.61303094)the Doctoral Fund ofMinistry of Education of China(No.20123108120027)+2 种基金the Program of Science and Technology Commission of Shanghai Municipality(No.14511107100)the Shanghai Leading Academic Discipline Project(No.J50103)the Innovation Program of Shanghai Municipal Education Commission(No.14YZ024)
文摘The mass data of social media and social networks generated by users play an important role in tracking users’sentiments and opinions online.A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments.Inspired by social cognitive theories,we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon.The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.
