Abstract
Extremely short comments (ESC) often contain rich information to convey users’ emotions towards content. However, conducting sentiment analysis on ESC is challenging due to the limited contextual semantic information and colloquial expressions. Traditional methods mainly focus on contextual text features. In this work, we propose a novel model, named Chinese Phonetic-Attentive Deep Fusion Network (CPADFN) that attentively fuse the Chinese phonetic alphabet features of the ESC, meta-information about the ESC along with the contextual text features. First, the multi-head self-attention mechanism is utilized to obtain the phonetic alphabet representation and the sentence representation separately. Also, a fully-connected layer is used on the embeddings of the meta-information about the ESC to obtain the meta-information representation. Then, the local activation unit is employed to attentively fuse these feature representations. Bi-LSTM is applied to address the sequence dependency across these fused features separately. Third, a fully-connected layer with softmax function is applied to predict emotional labels. We conduct experiments on a self-crawled ESC dataset DanmuCorpus, and two public Chinese short text datasets, MovieReview and WeiboCorpus. The experimental results demonstrate that CPADFN achieves better performances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bilibili homepage. https://www.bilibili.com/. Accessed 4 Jan 2021
Douyu homepage. https://www.douyu.com/. Accessed 4 Jan 2021
Niconico homepage. https://www.nicovideo.jp/. Accessed 4 Jan 2021
Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. In: WWW, pp. 757–766 (2007)
Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp. 69–78 (2014)
He, M., Ge, Y., Wu, L., Chen, E., Tan, C.: Predicting the popularity of danmu-enabled videos: a multi-factor view. In: DASFAA, pp. 351–366 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation, pp. 1735–1780 (1997)
Huang, Z., Zhao, H., Peng, F., Chen, Q., Zhao, G.: Aspect category sentiment analysis with self-attention fusion networks. In: DASFAA (2020)
Iida, S., Kimura, R., Cui, H., Hung, P.H., Utsuro, T., Nagata, M.: Attention over heads: a multi-hop attention for neural machine translation. In: ACL, pp. 217–222. ACL (2019)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751 (2014)
Li, C., Wang, J., Wang, H., Zhao, M., Li, W., Deng, X.: Visual-texual emotion analysis with deep coupled video and danmu neural networks. IEEE Transactions on Multimedia, pp. 1634–1646 (2019)
Li, X., Yang, B.: A pseudo label based dataless naive Bayes algorithm for text classification with seed words. In: COLING, pp. 1908–1917 (2018)
Lwowski, B., Rad, P., Choo, K.R.: Geospatial event detection by grouping emotion contagion in social media. IEEE Trans. Big Data pp. 159–170 (2020)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Peng, H., Ma, Y., Poria, S., Li, Y., Cambria, E.: Phonetic-enriched text representation for chinese sentiment analysis with reinforcement learning. ArXiv abs/1901.07880 (2019)
Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: WWW pp. 91–100 (2008)
Shen, D., et al.: Query enrichment for web-query classification. TOIS, pp. 320–352 (2006)
Sun, Y., Wang, S., Li, Y., Feng, S., Wu, H.: Ernie: Enhanced representation through knowledge integration. CoRR (2019)
Tian, Y., Song, Y., Xia, F., Zhang, T., Wang, Y.: Improving Chinese word segmentationwith wordhood memory networks. In: ACL, pp. 8274–8285 (2020)
Wu, B., Zhong, E., Tan, B., Horner, A., Yang, Q.: Crowdsourced time-sync video tagging using temporal and personalized topic modeling. In: SIGKDD, pp. 721–730 (2014)
Zhang, X., LeCun, Y.: Text understanding from scratch. ArXiv abs/1502.01710 (2015)
Zhang, Y., et al.: Learning Chinese word embeddings from stroke, structure and pinyin of characters. CIKM (2019)
Zheng, X., Chen, H., Xu, T.: Deep learning for Chinese word segmentation and POS tagging. In: EMNLP, pp. 647–657. ACL (2013)
Zhou, J., Xu, W.: End-to-end learning of semantic role labeling using recurrent neural networks. In: ACL, pp. 1127–1137 (2015)
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: ACL, p. 207 (2016)
Acknowledgment
This work is supported by the National Key Research and Development Program (2019YFB2102600) and the MOE Project of Key Research in Philosophy and Social Science (Grant No. 19JZD023).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hua, L., Chen, Q., Huang, Z., Zhao, H., Zhao, G. (2021). Not Only the Contextual Semantic Information: A Deep Fusion Sentimental Analysis Model Towards Extremely Short Comments. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-82147-0_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82146-3
Online ISBN: 978-3-030-82147-0
eBook Packages: Computer ScienceComputer Science (R0)