Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analy...Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark展开更多
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
The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users&#...The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.展开更多
Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and ...Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.展开更多
基金Tsinghua-Samsung Joint Laboratory, the National Basic Research 973 Program of China under Grant No. 2015CB358700, and the National Natural Science Foundation of China under Grant Nos. 61472206, 61073071, and 61303075.
文摘Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark
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.
文摘The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2015AA050204)
文摘Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.
