Abstract
Microblog (such as Weibo) is an integrated social platform of vital importance in the internet age. Because of its diversity, subjectivity and timeliness, microblog is popular among public. In order to perform sentiment classification on microblog posts and overcome the limitation of text information, a fine-grained sentiment analysis method is proposed, in which emoticon attributes are considered. Firstly, the microblog texts are pre-processed to remove some stop words and noise information such as links. Then the data is matched in the sentiment lexicon, and when the first matching succeeds, the second matching is performed in the emoticon dictionary. The emoticons in the emoticon dictionary are transformed into vector form. Through these matching, the emotional features are vectorized and other text features are considered. Finally, the iterative-based naive Bayesian classification method is used for sentiment classification. The experiment results show that emoticons have obvious effect on facilitating the sentiment classification of microblog posts, and the proposed sentiment classification method achieved better than average results in term of classification accuracy compared with state-of-art techniques.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adams B, Phung D, Venkatesh S (2014) Social reader: towards browsing the social web[J]. Multimedia Tools & Applications 69(3):951–990
Boia M, Musat CC, Faltings B (2014) Constructing context-aware sentiment lexicons with an asynchronous game with a purpose[J]. Computational Linguistics & Intelligent Text Processing 43(11):32–44
Di Fabbrizio G, Aker A, Gaizauskas R (2013) Summarizing online reviews using aspect rating distributions and language modeling[J]. IEEE Intell Syst 28(3):28–37
Feng S, Song K, Wang D et al (2015) A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs[J]. World Wide Web 18(4):949–967
Gui B, Yang XP, Zhang ZX (2014) Research on building lexicon for sentiment analysis based on the Chinese microblogging smiley[J]. Transactions of Beijing Institute of Technology 34(5):537–541
He Y, Zhang Y, Xiao M et al (2017) Infection relationship research on the emotions of different microblogging community: with the topic of “air Asia losing contact” as an example[J]. Statistics & Information Forum 32(8):110–116
Hong SY, Oh JC (2012) Comparative analysis on social network service users access : based on twitter, Facebook, KakaoStory[J]. Journal of Internet Computing & Services 13(6):9–16
Hu M, Liu B (2004) Mining and summarizing customer reviews[C]// proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, Washington, USA, august 22–25, 2004. ACM
Li Y, Liu R, Peng Z (2016) The research of community detection based on micro-blog users[C]// IEEE international conference on Progress in Informatics & Computing. IEEE: 129–137
Li Y Q, Li X, Han X, et al. (2016) A bilingual lexicon-based multi-class semantic orientation analysis for microblogs[J]. 44(9): 2068-2073
Li FF, Wang HT, Zhao RC et al (2017) Chinese micro-blog sentiment classification through a novel hybrid learning model[J]. J Cent South Univ 24(10):2322–2330
Liu JNK, Li BNL, Dillon TS (2001) An improved Naïve Bayesian classifier technique coupled with a novel input solution method[J]. IEEE Transactions on Systems Man & Cybernetics Part C 31(2):249–256
Liu QC, Huang HY, Feng C (2014) Multi-feature based sentiment orientation identification algorithm for micro-blog topics[J]. Journal of Chinese Information Processing 28(4):123–131
Liu SY, Tian Y, Feng YN et al (2018) Comparison of tourist thematic sentiment analysis methods based on weibo data[J]. Acta Sci Nat Univ Pekin 54(4):687–692
Mao LJ, Zeng WJ, Daniel D (2017) Personality-based refinement for sentiment classification in microblog[J]. Knowl-Based Syst 132:S0950705117303118
Moraes R, Valiati JF, Gavi O, Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN[J]. Expert Syst Appl 40(2):621–633
Pablos AG, Cuadros M, Claramunt GR (2015) Unsupervised word polarity tagging by exploiting continuous word representations[J]. Procesamiento De Lenguaje Natural 55(12):127–134
Quan C, Ren F (2010) Sentence emotion analysis and recognition based on emotion words using Ren-CECps[J]. International Journal of Advanced Intelligence Paradigms 2(1):105–117
Shaoliang S, Yimin W, Yuqing M (2015) Simple multi-label ranking for Chinese microblog sentiment classification[J]. Journal of Computer Applications 35(10):2721–2726
Sury U (2009) Social network services[J]. Informatik-Spektrum 32(3):267–273
Turney PD, Littman ML (2003) Measuring praise and criticism[J]. ACM Trans Inf Syst 21(4):315–346
Wang XM, Zuo WL, Wang Y et al (2013) Microblog comments sentiment analysis based on extended emotional lexicon[J]. Communications in Computer & Information Science 39(2):385–397
Yessenalina A , Choi Y, Cardie C. (2010) Automatically Generating Annotator Rationales to Improve Sentiment Classification[C]// ACL 2010, Proceedings of the 48th annual meeting of the Association for Computational Linguistics, July 11-16, 2010, Uppsala, Sweden, Short Papers Association for Computational Linguistics
Zhang H, Tan Y (2018) Implement intelligent dynamic analysis of bottom-hole pressure with naive Bayesian models[J]. Multimedia Tools & Applications 53(2):209–218
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, Y. Iteration-based naive Bayes sentiment classification of microblog multimedia posts considering emoticon attributes. Multimed Tools Appl 79, 19151–19166 (2020). https://doi.org/10.1007/s11042-020-08797-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-08797-7