Abstract
The texts of social media event have feathers of massive-sparse, dynamic-heterogeneous, and obscure-vague, which increase the difficulty of event emotion computing. Aiming at the problem, we construct the dictionary supervised emotion computing model, which can be applied in hot news mining and public opinion guidance analysis based on sentiment computing in network social media. The text words and labels are used as the input of the models, and the profile distribution and emotion distribution of the texts, the word distribution of the profiles, and emotions are output by the models. In addition, the words with definite emotion are used as the constraint condition of the model to enhance the accuracy of text emotion calculation. Our proposed algorithm can express the emotion of the text by using the words and labels from labeled texts, and the emotion words value is calculated through a finite iteration of the network. We also make use of the word emotion in the basic word emotion dictionary to modify the network and then recompute the word emotion, which effectively overcomes the problem of emotion uncertainty of the traditional methods. Experiments show that the accuracy of our model is generally higher than that of ETM, MSTM, and SLTM. Therefore, our proposed method is effective and feasible.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Kim Y (2014) Convolutional neural networks for sentence classification. Empirical Methods in Natural Language Processing 25(8) :1746–1751
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113
Saif H et al (2016) Contextual semantics for sentiment analysis of Twitter. Inf Process Manag 52(1):5–19
Martinezcamara E et al (2014) Sentiment analysis in Twitter. Nat Lang Eng 20(1):1–28
Wollmer, Martin, et al. “YouTube movie reviews: sentiment analysis in an audio-visual context.” IEEE Intell Syst 28.3 (2013): 46–53.
Munezero M, Montero CS, Mozgovoy M, et al. EmoTwitter – A fine-grained visualization system for identifying enduring sentiments in tweets. [C]// Computational Linguistics & Intelligent Text Processing. 2015
Cambria E et al (2012) Sentic computing for social media mark theory. Multimedia Tools and Applications 59(2):557–577
Lin L et al (2014) Opinion mining and sentiment analysis in social networks: a retweeting structure-aware approach. IEEE/ACM International Conference Utility and Cloud Computing 890–895
Zhou G et al (2016) Cross-lingual sentiment classification with stacked autoencoders. Knowl Inf Syst 47(1):27–44
Hochreiter R (2015) Computing trading strategies based on financial sentiment data using evolutionary optimization. Soft Comput 32:181–191
Ain QT, Ali M, Riaz A et al (2017) Sentiment analysis using deep learning techniques: a review [J]. Int J Adv Comput Sci Appl 8(6):1011–1026
Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107
Palanisamy P, Vineet Y, Harsha E (2013) "Serendio: simple and practical lexicon based approach to sentiment analysis." Joint Conference on Lexical and Computational Semantics 543–548.
Arras L, et al. (2017) "Explaining recurrent neural network predictions in sentiment analysis.." Empirical Methods in Natural Language Processing 159–168
Rani S, Kumar P (2017) A sentiment analysis system to improve teaching and learning. IEEE Comput 50(5):36–43
Paolanti M, et al. (2017) "Visual and textual sentiment analysis of brand-related social media pictures using deep convolutional neural networks." International Conference on Image Analysis and Processing 402–413
Wang B et al (2016) A multi-granularity fuzzy computing model for sentiment classification of Chinese reviews. Journal of Intelligent and Fuzzy Systems 30(3):1445–1460
Sehgal D, Agarwal A K (2015) "Sentiment analysis of big data applications using Twitter data with the help of HADOOP framework." International Conference System Modeling & Advancement Research Trends (2016): 251-255.
Yu Y, Wang X, World Cup (2014) In the Twitter World: a big data analysis of sentiments in US sports fans’ tweets [J]. Comput Hum Behav 48:392–400
Liu SM, Chen JH (2015) A multi-label classification based approach for sentiment classification [J]. Expert Syst Appl 42(3):1083–1093
Luo X, Xu Z, Yu J et al (2011) Building association link network for semantic link on web resources [J]. IEEE Trans Autom Sci Eng 8(3):482–494
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation [J]. J Mach Learn Res 3:993–1022
Yildirim I (2012) Bayesian Inference: Metropolis-Hastings Sampling [J]. Dept. of Brain and Cognitive Sciences, Univ. of Rochester, Rochester
Pool C, Nissim M (2016) Distant supervision for emotion detection using Facebook reactions [J]. arXiv preprint arXiv 34:1611–1625
Casella G, George EI (1992) Explaining the Gibbs sampler [J]. Am Stat 46(3):167–174
Rao Y, Li Q, Mao X, Wenyin L (2014) Sentiment topic models for social emotion mining [J]. Inf Sci 266(5):90–100
Bao S, Xu S, Zhang L et al (2011) Mining social emotions from affective text [J]. IEEE transactions on Knowledge & Data. Engineering 24(9):1658–1670
Funding
This paper was funded by Zhejiang Public Welfare Technology Application Research Project (No. LGG19F020009).
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
Feng, Z. Hot news mining and public opinion guidance analysis based on sentiment computing in network social media. Pers Ubiquit Comput 23, 373–381 (2019). https://doi.org/10.1007/s00779-018-01192-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-018-01192-y