[Objective] This paper tries to effectively analyze the sentiment of trending topics with machine learning techniques. [Methods] First, we proposed a new classification model based on trending topic relevance to extract subjective microblog posts. Second, we analyzed sentiment tendency with an improved machine learning method. [Results] We found that the modified model improved the subjective-objective classification of trending topics. The F-measures were increased by 7.4% and 2.2% respectively. [Limitations] More research is needed to study the distribution of data, the particle of emotion and the changes of sentiment trends. [Conclusions] Adding topic relevance factor to the model could improve the performance of sentiment analysis of micro-blog posts, and extract tendency of key objects from the trending topics, which provides intelligence for micro-blog marketing.
何跃, 肖敏, 张月. 结合话题相关性的热点话题情感倾向研究*[J]. 数据分析与知识发现, 2017, 1(3): 46-53.
He Yue,Xiao Min,Zhang Yue. Sentiment Analysis of Trending Topics Based on Relevance. Data Analysis and Knowledge Discovery, 2017, 1(3): 46-53.
(Gui Bin, Yang Xiaoping, Zhang Zhongxia, et al.Research on Building Lexicon for Sentiment Analysis Based on the Chinese Microblogging Smiley[J]. Transactions of Beijing Institute of Technology, 2014, 34(5): 537-541.)
[3]
Bravo-Marquez F, Frank E, Pfahringer B.Building a Twitter Opinion Lexicon from Automatically-annotated Tweets[J]. Knowledge-Based Systems, 2016, 108(SI). DOI: 10.1016/j.knosys.2016.05.018.
doi: 10.1016/j.knosys.2016.05.018
(Ning Hui, Yang Song, Zhao Yong, et al.Study of Microblog Sentiment Analysis Based on Semantic Feature[J]. Applied Science and Technology, 2016, 43(3): 70-74.)
doi: 10.11991/yykj.201506036
[5]
Zhou Z, Zhang X, Sanderson M.Sentiment Analysis on Twitter Through Topic-Based Lexicon Expansion[A]// Databases Theory and Applications[M]. Springer International Publishing, 2014:98-109.
[6]
Saif H, Fernandez M, He Y, et al.SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter[A]// The Semantic Web: Trends and Challenges[M]. Springer, Cham, 2014: 83-98.
[7]
Saif H, He Y, Fernandez M, et al.Adapting Sentiment Lexicons Using Contextual Semantics for Sentiment Analysis of Twitter[A]// The Semantic Web: ESWC 2014 Satellite Events[M]. Springer, Cham, 2014: 54-63.
[8]
Saif H, He Y, Fernandez M, et al.Contextual Semantics for Sentiment Analysis of Twitter[J]. Information Processing & Management, 2015, 52(1): 5-19.
doi: 10.1016/j.ipm.2015.01.005
[9]
Saif H, Fernandez M, Kastler L, et al.A Linked Open Data Approach for Sentiment Lexicon Adaptation[C]// Proceedings of the 15th International Semantic Web Conference. 2016.
[10]
Zhao J, Cao X.Combining Semantic and Prior Polarity for Boosting Twitter Sentiment Analysis[C]//Proceedings of the 2015 IEEE International Conference on Smart City/ Socialcom/Sustaincom. IEEE, 2015:832-837.
[11]
Le B, Nguyen H.Twitter Sentiment Analysis Using Machine Learning Techniques[A]// Advanced Computational Methods for Knowledge Engineering [M]. Springer International Publishing, 2015: 279-289.
[12]
Qasem M, Thulasiram R, Thulasiram P.Twitter Sentiment Classification Using Machine Learning Techniques for Stock Markets[C]//Proceedings of the 2015 International Conference on Advances in Computing, Communications and Informatics. IEEE, 2015.
[13]
Palguna D, Joshi V, Chakaravarthy V, et al.Analysis of Sampling Algorithms for Twitter[C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence. AAAI Press, 2015.
[14]
Song K, Feng S, Gao W, et al.Personalized Sentiment Classification Based on Latent Individuality of Microblog Users[C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence. AAAI Press, 2015.
[15]
Abdelwahab O, Bahgat M, Lowrance C J, et al.Effect of Training Set Size on SVM and Naive Bayes for Twitter Sentiment Analysis[C]// Proceedings of the IEEE International Symposium on Signal Processing and Information Technology. 2015: 46-51.
[16]
Saif H, He Y, Alani H, et al.On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter[C]// Proceedings of the 9th International Conference on Language Resources and Evaluation. 2014.
[17]
Ah-Pine J, Morales E P S. A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis[C]// Proceedings of the Workshop on Interactions Between Data Mining and Natural Language Processing. 2016.
[18]
Sabariah M K, Effendy V.Sentiment Analysis on Twitter Using the Combination of Lexicon-based and Support Vector Machine for Assessing the Performance of a Television Program[C]//Proceedings of the International Conference on Information and Communication Technology. 2015.
[19]
张想. 面向热点话题型微博的情感分析研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.
[19]
(Zhang Xiang.Research on Sentiment Analysis for Hot Topic Microblog[D]. Harbin: Harbin Institute of Technology, 2013.)
(Wu Qinglin, Wang Yan.Research on the Emotional Feature Selection Method in the Chinese Microblog[J]. Journal of Inner Mongolia Normal University: Natural Science Edition, 2016, 45(1): 84-88.)
doi: 10.3969/j.issn.1671-5896.2010.06.011
(Tian Jiule, Zhao Wei.Words Similarity Algorithm Based on Tongyici Cilin in Semantic Web Adaptive Learning System[J]. Journal of Jilin University: Information Science Edition, 2010, 28(6): 602-608.)
doi: 10.3969/j.issn.1671-5896.2010.06.011