Abstract
As a tool to study people’s views and opinions on things and events around them, sentiment analysis is widely used in the analysis and processing of mass evaluation information. Traditional emotion analysis is generally based on emotion dictionary, but the construction of emotion dictionary needs a lot of artificial time, and in different application fields need to establish different emotion dictionaries. Meanwhile, emotion dictionary can’t contain the semantic information of words and it also ignore the role of non-emotional words in the expression of emotion. This paper proposes a new emotion analysis algorithm (CBOW-PE) based on word embedding and part of speech. First, in the stage of pre-processed experiment, we use part of speech to preprocess the experimental data, fully consider the role of non-emotional words on emotion analysis. And then we add emotional information to assist word vector training, so that the word embedding of words related to emotion has both semantic information and emotional information. Finally, this paper makes a lot of experimental analysis and comparison of this paper and its related algorithms. The results show that the algorithm is effective and efficient in Chinese emotion analysis.
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References
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Jain, A.K., Pandey, K.: Analysis and implementation of sentiment classification using lexical POS markers. Int. J. 2(1), 36–40 (2013)
Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242 (2013)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Bengio, Y.: Deep learning of representations: looking forward. In: Dediu, A.H., Martín-Vide, C., Mitkov, R., Truthe, B. (eds.) SLSP 2013. LNCS (LNAI), vol. 7978, pp. 1–37. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39593-2_1
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Tang, D., Wei, F., Qin, B., Zhou, M., Liu, T.: Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of Coling 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 172–182 (2014)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018)
Badr, B.M, Fatima, S.S.: Using skipgrams, bigrams, and part of speech features for sentiment classification of twitter messages. In: Sharma, D.M, Sangal, R., Sherly, E. (eds.) Proceedings of the 12th International Conference on Natural Language Processing, ICON 2015, Trivandrum, India, 11–14 December 2015, pp. 268–275. NLP Association of India (2015)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp, 151–160. Association for Computational Linguistics (2011)
Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 607–618 (2013)
Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086 (2018)
Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461 (2017)
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. (TOIS) 21(4), 315–346 (2003)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
Bollegala, D., Mu, T., Goulermas, J.Y.: Cross-domain sentiment classification using sentiment sensitive embeddings. IEEE Trans. Knowl. Data Eng. 28(2), 398–410 (2015)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Zheng, X., Chen, H., Xu, T.: Deep learning for Chinese word segmentation and POS tagging. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 647–657 (2013)
Liu, T., Cui, Y., Yin, Q., Zhang, W., Wang, S., Hu, G.: Generating and exploiting large-scale pseudo training data for zero pronoun resolution. arXiv preprint arXiv:1606.01603 (2016)
Narasimhan, K., Yala, A., Barzilay, R.: Improving information extraction by acquiring external evidence with reinforcement learning. arXiv preprint arXiv:1603.07954 (2016)
Goldberg, Y., Levy, O.: Word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. CoRR, abs/1402.3722 (2014)
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Guo, J., Xia, Z. (2020). An Algorithm for Emotion Evaluation and Analysis Based on CBOW. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_31
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