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SEMTec: Social Emotion Mining Techniques for Analysis and Prediction of Facebook Post Reactions

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Agents and Artificial Intelligence (ICAART 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11352))

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Abstract

Nowadays social media are utilized by many people in order to review products and services. Subsequently, companies can use this feedback in order to improve customer experience. Facebook provided its users with the ability to express their experienced emotions by using five so-called ‘reactions’. Since this launch happened in 2016, this paper is one of the first approaches to provide a complete framework for evaluating different techniques for predicting reactions to user posts on public pages. For this purpose, we used the FacebookR dataset that contains Facebook posts (along with their comments and reactions) of the biggest international supermarket chains. In order to build a robust and accurate prediction pipeline state-of-the-art neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. The models are further improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post and a data augmentation technique to obtain an even more robust predictor. The final proposed pipeline is a combination of a neural network and a baseline emotion miner and is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.1326.

T. Moers and F. Krebs—Equal contribution.

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Notes

  1. 1.

    http://newsroom.fb.com/news/2016/02/reactions-now-available-globally/.

  2. 2.

    https://github.com/jerryspan/FacebookR.

References

  1. Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)

    Article  Google Scholar 

  2. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56, 82–89 (2013)

    Article  Google Scholar 

  3. Troussas, C., Virvou, M., Espinosa, K.J., Llaguno, K., Caro, J.: Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In: 2013 Fourth International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6. IEEE (2013)

    Google Scholar 

  4. Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57, 74–81 (2014)

    Article  Google Scholar 

  5. Krebs, F., Lubascher, B., Moers, T., Schaap, P., Spanakis, G.: Social emotion mining techniques for Facebook posts reaction prediction. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, pp. 211–220 (2018)

    Google Scholar 

  6. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)

    Article  Google Scholar 

  7. Kim, Y.: Convolutional neural networks for sentence classification. CoRR abs/1408.5882 (2014)

    Google Scholar 

  8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  9. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  10. Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 6, 107–116 (1998)

    Article  Google Scholar 

  11. Wang, G., Sun, J., Ma, J., Xu, K., Gu, J.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. 57, 77–93 (2014)

    Article  Google Scholar 

  12. Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040. ACM (2011)

    Google Scholar 

  13. Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the omg!. Icwsm 11, 164 (2011)

    Google Scholar 

  14. Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35176-1_32

    Chapter  Google Scholar 

  15. Sarlan, A., Nadam, C., Basri, S.: Twitter sentiment analysis. In: 2014 International Conference on Information Technology and Multimedia (ICIMU), pp. 212–216. IEEE (2014)

    Google Scholar 

  16. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  17. Canales, L., Strapparava, C., Boldrini, E., Martínez-Barco, P.: Exploiting a bootstrapping approach for automatic annotation of emotions in texts. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 726–734 (2016)

    Google Scholar 

  18. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  19. Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., Sun, C.: Facebook sentiment: Reactions and emojis. In: SocialNLP 2017, p. 11 (2017)

    Google Scholar 

  20. Pool, C., Nissim, M.: Distant supervision for emotion detection using Facebook reactions. arXiv preprint arXiv:1611.02988 (2016)

  21. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  22. Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 513–520 (2011)

    Google Scholar 

  23. Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)

    Google Scholar 

  24. Yang, C., Lin, K.H.Y., Chen, H.H.: Emotion classification using web blog corpora. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 275–278. IEEE (2007)

    Google Scholar 

  25. Wen, S., Wan, X.: Emotion classification in microblog texts using class sequential rules. In: AAAI, pp. 187–193 (2014)

    Google Scholar 

  26. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10 (2010)

    Google Scholar 

  27. Yang, Z., Fang, X.: Online service quality dimensions and their relationships with satisfaction: a content analysis of customer reviews of securities brokerage services. Int. J. Serv. Indus. Manag. 15, 302–326 (2004)

    Article  Google Scholar 

  28. 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. ACM (2004)

    Google Scholar 

  29. Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28, 15–21 (2013)

    Article  Google Scholar 

  30. Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning (2017)

    Google Scholar 

  31. Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: BAGAN: Data Augmentation with Balancing GAN. ArXiv e-prints (2018)

    Google Scholar 

  32. Antoniou, A., Storkey, A., Edwards, H.: Data Augmentation Generative Adversarial Networks. ArXiv e-prints (2017)

    Google Scholar 

  33. Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Synthetic data augmentation using GAN for improved liver lesion classification. CoRR abs/1801.02385 (2018)

    Google Scholar 

  34. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification. ArXiv e-prints (2018)

    Google Scholar 

  35. Zhang, X., LeCun, Y.: Text understanding from scratch (2015) cite arxiv:1502.01710 Comment: This technical report is superseded by a paper entitled “Character-level Convolutional Networks for Text Classification”, arXiv:1509.01626. It has considerably more experimental results and a rewritten introduction

  36. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)

    Google Scholar 

  37. Singh, T., Kumari, M.: Role of text pre-processing in Twitter sentiment analysis. Procedia Comput. Sci. 89, 549–554 (2016)

    Google Scholar 

  38. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media, Inc., Sebastopol (2009)

    Google Scholar 

  39. Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)

    Google Scholar 

  40. Farooq, U., Nongaillard, A., Ouzrout, Y., Qadir, M.A.: Negation handling in sentiment analysis at sentence level. In: International Conference on Information Management, London, United Kingdom (2016)

    Google Scholar 

  41. Arora, S., Yingyu Liang, T.M.: A simple but tough-to-beat baseline for sentence embeddings (2017)

    Google Scholar 

  42. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24, 513–523 (1988)

    Article  Google Scholar 

  43. Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

  44. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  45. Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)

  46. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)

    Google Scholar 

  47. Masnadi-Shirazi, H., Vasconcelos, N.: On the design of loss functions for classification: theory, robustness to outliers, and savageboost. In: Advances in Neural Information Processing Systems, pp. 1049–1056 (2009)

    Google Scholar 

  48. Deng, L., Wiebe, J.: MPQA 3.0: an entity/event-level sentiment corpus. In: Mihalcea, R., Chai, J.Y., Sarkar, A. (eds.) HLT-NAACL, The Association for Computational Linguistics, pp. 1323–1328 (2015)

    Google Scholar 

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Authors and Affiliations

  1. Department of Data Science and Knowledge Engineering, Maastricht University, 6200 MD, Maastricht, The Netherlands

    Tobias Moers, Florian Krebs & Gerasimos Spanakis

Authors
  1. Tobias Moers
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  2. Florian Krebs
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  3. Gerasimos Spanakis
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Corresponding author

Correspondence to Gerasimos Spanakis .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. University of Porto, Porto, Portugal

    Ana Paula Rocha

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Moers, T., Krebs, F., Spanakis, G. (2019). SEMTec: Social Emotion Mining Techniques for Analysis and Prediction of Facebook Post Reactions. In: van den Herik, J., Rocha, A. (eds) Agents and Artificial Intelligence. ICAART 2018. Lecture Notes in Computer Science(), vol 11352. Springer, Cham. https://doi.org/10.1007/978-3-030-05453-3_17

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  • DOI: https://doi.org/10.1007/978-3-030-05453-3_17

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