|
|
|
| Sentiment Classification of Micro-blog Public Opinion Based on Convolution Neural Network |
| Zhang Haitao1,2, Wang Dan1, Xu Hailing1, Sun Siyang1 |
1. Management School of Jilin University, Changchun 130022;
2. The Information Resource Research Center of Jilin University, Changchun 130022 |
|
|
|
|
Abstract In this paper, a sentiment classification model of micro-blog public sentiment is constructed based on convolution neural network. Micro-blog topic data is obtained by crawling and using word2vec to train word vectors. NLPIR/ICTCLAS 2016 tools were used for word segmentation and subsequently MATLAB programming model training and testing. The results show that the model can achieve an effective sentiment classification of micro-blog public sentiment, which is superior to traditional machine learning.
|
|
Received: 11 May 2018
|
|
|
|
[1] Vermeulen A, Vandebosch H, Heirman W.#Smiling, #venting, or both? Adolescents’ social sharing of emotions on social media[J]. Computers in Human Behavior, 2018, 84: 211-219.
[2] Sun X, Zhang C, Li G Q, et al.Detecting users’ anomalous emotion using social media for business intelligence[J]. Journal of Computational Science, 2018, 25: 193-200.
[3] Mohammad S M, Kiritchenko S.Using hashtags to capture fine emotion categories from tweets[J]. Computational Intelligence, 2015, 31(2): 301-326.
[4] Yang X, Macdonald C, Ounis I.Using word embeddings in Twitter election classification[J]. Information Retrieval Journal, 2018, 21(2-3): 183-207.
[5] 吴青林, 周天宏. 基于话题聚类及情感强度的中文微博舆情分析[J]. 情报理论与实践, 2016, 39(1): 109-112.
[6] 周瑛, 刘越, 蔡俊. 基于注意力机制的微博情感分析[J]. 情报理论与实践, 2018, 41(3): 89-94.
[7] 蔡瑶, 吴鹏, 王佳敏, 等. 基于ACT-R理论模型的微博网民负面情感认知决策过程研究[J]. 情报科学, 2018, 36(1): 135-140.
[8] 王英, 龚花萍. 基于情感维度的大数据网络舆情情感倾向性分析研究——以“南昌大学自主保洁”微博舆情事件为例[J]. 情报科学, 2017, 35(4): 37-42.
[9] 陈娟, 刘燕平, 邓胜利. 政府辟谣信息的用户评论及其情感倾向的影响因素研究[J]. 情报科学, 2017, 35(12): 61-65.
[10] 梁晓敏, 徐健. 舆情事件中评论对象的情感分析及其关系网络研究[J]. 情报科学, 2018, 36(2): 37-42.
[11] Carr A.Understanding emotion and emotionality in a process of change[J]. Journal of Organizational Change Management, 2017, 14(5): 421-436.
[12] 张石清, 李乐民, 赵知劲. 人机交互中的语音情感识别研究进展[J]. 电路与系统学报, 2013, 18(2): 440-451.
[13] Ekman P.Facial expression and emotion[J]. American Psychologist, 1993, 48(4): 384-92.
[14] Quan C Q, Ren F J.Construction of a blog emotion corpus for Chinese emotional expression analysis[C]// Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2009, 3: 1446-1454.
[15] Ortony A, Clore G L, Collins A.The cognitive structure of emotions[J]. Contemporary Sociology, 1988, 18(6): 2147-2153.
[16] 徐琳宏. 基于语义资源的文本情感计算[D]. 大连: 大连理工大学, 2007.
[17] Chen Y H, Krishna T, Emer J S, et al.Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks[J]. IEEE Journal of Solid-State Circuits, 2017, 52(1): 127-138.
[18] Lecun Y, Bottou L, Bengio Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[19] 李枫林, 柯佳. 基于深度学习框架的实体关系抽取研究进展[J]. 情报科学, 2018, 36(3): 169-176.
[20] 吴鹏, 刘恒旺, 沈思. 基于深度学习和OCC情感规则的网络舆情情感识别研究[J]. 情报学报, 2017, 36(9): 972-980.
[21] Kim Y.Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics,,2014: 1746-1751.
[22] 蔡国永, 夏彬彬. 基于卷积神经网络的图文融合媒体情感预测[J]. 计算机应用, 2016, 36(2): 428-431, 477.
[23] 李杰, 李欢. 基于深度学习的短文本评论产品特征提取及情感分类研究[J]. 情报理论与实践, 2018(2): 143-148.
[24] Lecun Y, Bengio Y, Hinton G.Deep learning[J]. Nature, 2015, 521(7553): 436. |
|
|
|
