Abstract
The emerging microblogging service provides a new channel for people to share opinions and sentiment. As a result, microblog sentiment analysis has become a cutting-edge and popular research field, which has many important applications. Existing methods mostly extract sophisticated features from microblog texts without considering that microblogs are networked data, which suffer from poor performance. To address this issue, we propose a new model that assumes microblogs are interconnected and that connected microblogs are more likely to share the same sentiment. We leverage two types of information to model the connections between microblogs: user information and friend information. Our assumption is supported by two sociological theories: sentiment consistency and emotional contagion. The connections between microblogs based on user and friend information are often sparse and noisy, which can limit the effectiveness of sentiment analysis. To mitigate this issue, we use link prediction to identify potential connections between microblogs and introduce a sentiment connection weights matrix to quantify the degree of sentiment difference between connected microblogs. We then integrate potential social links and sentiment connection weights into our content-based sentiment model using a Laplacian regularization term. To demonstrate the effectiveness, sufficient experiments are conducted on two real datasets to show that exploring potential links and introducing sentiment connection weights can improve the performance of microblog sentiment analysis significantly.
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References
Yang, D., Zhang, D., Yu, Z., Wang, Z.: A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, HT 2013, Paris, France, pp. 119–128 (2013)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Cambria, E., Schuller, B., Xia, Y., White, B.: New avenues in knowledge bases for natural language processing. Knowl.-Based Syst. 108, 1–4 (2016)
Wu, Y., Liu, S., Yan, K., Liu, M., Wu, F.: OpinionFlow: visual analysis of opinion diffusion on social media. IEEE Trans. Vis. Comput. Graph. 20(12), 1763–1772 (2014)
Cambria, E., Liu, Q., Decherchi, S., Xing, F., Kwok, K.: SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3829–3839 (2022)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), vol. 10, pp. 2200–2204 (2010)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
Marsden, P.V., Friedkin, N.E.: Network studies of social influence. Sociol. Methods Res. 22(1), 127–151 (1993)
Hatfield, E., Cacioppo, J.T., Rapson, R.L.: Emotional contagion. Curr. Dir. Psychol. Sci. 2(3), 96–100 (1993)
Abelson, R.P.: Whatever became of consistency theory? Pers. Soc. Psychol. Bull. 9(1), 37–64 (1983)
Speriosu, M., Sudan, N., Upadhyay, S., Baldridge, J.: Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the First Workshop on Unsupervised Learning in NLP, EMNLP 2011, pp. 53–63 (2011)
Song, C., Wang, B., Jiang, Q., Zhang, Y., He, R., Hou, Y.: Social recommendation with implicit social influence. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1788–1792 (2021)
Khalifi, H., Dahir, S., El Qadi, A., Ghanou, Y.: Enhancing information retrieval performance by using social analysis. Soc. Netw. Anal. Min. 10, 1–7 (2020)
Mehta, N., Pacheco, M.L., Goldwasser, D.: Tackling fake news detection by continually improving social context representations using graph neural networks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1363–1380 (2022)
Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, pp. 537–546 (2013)
Wu, F., Huang, Y., Song, Y.: Structured microblog sentiment classification via social context regularization. Neurocomputing 175(PartA), 599–609 (2016)
Cui, A., Zhang, M., Liu, Y., Ma, S.: Emotion tokens: bridging the gap among multilingual Twitter sentiment analysis. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds.) AIRS 2011. LNCS, vol. 7097, pp. 238–249. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25631-8_22
Wu, F., Huang, Y., Song, Y., Liu, S.: Towards building a high-quality microblog-specific Chinese sentiment lexicon. Decis. Support Syst. 87, 39–49 (2016)
Xing, F.Z., Pallucchini, F., Cambria, E.: Cognitive-inspired domain adaptation of sentiment lexicons. Inf. Process. Manag. 56(3), 554–564 (2019)
Feng, S., Wang, Y., Liu, L., Wang, D., Yu, G.: Attention based hierarchical LSTM network for context-aware microblog sentiment classification. World Wide Web 22(1), 59–81 (2019)
Ma, Y., Peng, H., Khan, T., Cambria, E., Hussain, A.: Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cogn. Comput. 10(4), 639–650 (2018)
Shin, B., Lee, T., Choi, J.D.: Lexicon integrated CNN models with attention for sentiment analysis, arXiv preprint arXiv:1610.06272 (2016)
Wang, L., Niu, J., Yu, S.: SentiDiff: combining textual information and sentiment diffusion patterns for Twitter sentiment analysis. IEEE Trans. Knowl. Data Eng. 32(10), 2026–2039 (2019)
Wu, F., Huang, Y.: Personalized microblog sentiment classification via multi-task learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 3059–3065 (2016)
Fersini, E., Pozzi, F., Messina, E.: Approval network: a novel approach for sentiment analysis in social networks. World Wide Web 20(4), 831–854 (2017)
Cheng, K., Li, J., Tang, J., Liu, H.: Unsupervised sentiment analysis with signed social networks. In: 31st AAAI Conference on Artificial Intelligence, pp. 3429–3435 (2017)
Skianis, K., Rousseau, F., Vazirgiannis, M.: Regularizing text categorization with clusters of words. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1827–1837 (2016)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. J. R. Stat. Soc. 167(1), 192 (2001)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Zhou, T., Lü, L., Zhang, Y.-C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging. Sci. 2(1), 183–202 (2009)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)
Keramatfar, A., Amirkhani, H., Bidgoly, A.J.: Modeling tweet dependencies with graph convolutional networks for sentiment analysis. Cogn. Comput. 14, 2234–2245 (2022)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Cs224n Project Report (2009)
Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, pp. 1746–1751 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations, pp. 1–14 (2017)
Acknowledgement
This paper is supported by 1) Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ23F020039, 2) National Science and Technology Major Project of China under Grant No. 2021ZD0114303, 3) National Natural Science Foundation of China under Grant No. 62176087.
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Zou, X., Li, T., Yang, J. (2023). Social Links Enhanced Microblog Sentiment Analysis: Integrating Link Prediction and Sentiment Connection Weights. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14146. Springer, Cham. https://doi.org/10.1007/978-3-031-39847-6_23
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