People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applica...People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.展开更多
Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new sour...Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.展开更多
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research was supported in part by National Basic Research Program of China （973 Program, No. 2013CB329601, No. 2013CB329604）, National Natural Science Foundation of China （No.91124002, 61372191, 61472433, 61202362, 11301302）, and China Postdoctoral Science Foundation （2013M542560）. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
文摘People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.
基金the National Natural Sci-ence Foundation of China(82041020,72088101,91846301)XL ac-knowledges support from the National Natural Science Foundation of China(72025405,71771213)+3 种基金the Hunan Science and Technol-ogy Plan Project(2020JJ4673,2020TP1013)JL was supported by the National Natural Science Foundation of China(61773248)the Major Program of National Fund of Philosophy and Social Sci-ence of China(20ZDA060)TC and XT were supported by the Shen-zhen Basic Research Project for Development of Science and Technology(JCYJ20200109141218676).
文摘Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.
