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An Ensemble Based Classification Approach for Persian Sentiment Analysis

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Progresses in Artificial Intelligence and Neural Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 184))

Abstract

In recent years, sentiment analysis received a great deal of attention due to the accelerated evolution of the Internet, by which people all around the world share their opinions and comments on different topics such as sport, politics, movies, music and so on. The result is a huge amount of available unstructured information. In order to detect positive or negative subject’s sentiment from this kind of data, sentiment analysis technique is widely used. In this context, here, we introduce an ensemble classifier for Persian sentiment analysis using shallow and deep learning algorithms to improve the performance of the state-of-art approaches. Specifically, experimental results show that the proposed ensemble classifier achieved accuracy rate up to 79.68%.

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References

  1. Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., Gupta, B.: Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels reviews. J. Comput. Sci. 27, 386–393 (2018)

    Google Scholar 

  2. Al-Smadi, M., Talafha, B., Al-Ayyoub, M., Jararweh, Y.: Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int. J. Mach. Learn. Cybern. 1–13 (2018)

    Google Scholar 

  3. Alimardani, S., Aghaie, A.: Opinion mining in Persian language using supervised algorithms (2015)

    Google Scholar 

  4. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv:1607.04606 (2016)

  5. Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  6. Chen, H., Li, S., Wu, P., Yi, N., Li, S., Huang, X.: Fine-grained sentiment analysis of Chinese reviews using LSTM network. J. Eng. Sci. Technol. Rev. 11(1) (2018)

    Google Scholar 

  7. Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., Durrani, T.: A comparative study of Persian sentiment analysis based on different feature combinations. In: International Conference in Communications, Signal Processing, and Systems, pp. 2288–2294. Springer (2017)

    Google Scholar 

  8. Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., Hussain, A.: Exploiting deep learning for Persian sentiment analysis. In: International Conference on Brain Inspired Cognitive Systems, pp. 597–604. Springer (2018)

    Google Scholar 

  9. Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A.Y., Cambria, E.: PerSent: a freely available Persian sentiment lexicon. In: International Conference on Brain Inspired Cognitive Systems, pp. 310–320. Springer (2016)

    Google Scholar 

  10. Dashtipour, K., Poria, S., Hussain, A., Cambria, E., Hawalah, A.Y., Gelbukh, A., Zhou, Q.: Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn. Comput. 8(4), 757–771 (2016)

    Article  Google Scholar 

  11. Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69–78 (2014)

    Google Scholar 

  12. Dragoni, M., Petrucci, G.: A fuzzy-based strategy for multi-domain sentiment analysis. Int. J. Approx. Reason. 93, 59–73 (2018)

    Article  MathSciNet  Google Scholar 

  13. García-Pablos, A., Cuadros, M., Rigau, G.: W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Syst. Appl. 91, 127–137 (2018)

    Article  Google Scholar 

  14. Gasparini, S., Campolo, M., Ieracitano, C., Mammone, N., Ferlazzo, E., Sueri, C., Tripodi, G., Aguglia, U., Morabito, F.: Information theoretic-based interpretation of a deep neural network approach in diagnosing psychogenic non-epileptic seizures. Entropy 20(2), 43 (2018)

    Article  Google Scholar 

  15. Gogate, M., Adeel, A., Hussain, A.: Deep learning driven multimodal fusion for automated deception detection. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6. IEEE (2017)

    Google Scholar 

  16. Gogate, M., Adeel, A., Marxer, R., Barker, J., Hussain, A.: DNN driven speaker independent audio-visual mask estimation for speech separation. arXiv:1808.00060 (2018)

  17. Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R., Mihalcea, R.: Cascade: contextual sarcasm detection in online discussion forums. arXiv:1805.06413 (2018)

  18. Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F.C., Larijani, H., Raza, A., Hussain, A.: Statistical analysis driven optimized deep learning system for intrusion detection. In: International Conference on Brain Inspired Cognitive Systems, pp. 759–769. Springer (2018)

    Google Scholar 

  19. Ieracitano, C., Adeel, A., Morabito, F.C., Hussain, A.: A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing (2019)

    Google Scholar 

  20. Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., Morabito, F.C.: A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing 323, 96–107 (2019)

    Article  Google Scholar 

  21. Ieracitano, C., Mammone, N., Hussain, A., Morabito, F.C.: A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw. (2019)

    Google Scholar 

  22. Kirilenko, A.P., Stepchenkova, S.O., Kim, H., Li, X.: Automated sentiment analysis in tourism: comparison of approaches. J. Travel Res. 57(8), 1012–1025 (2018)

    Article  Google Scholar 

  23. Li, B., Dimitriadis, D., Stolcke, A.: Acoustic and lexical sentiment analysis for customer service calls. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5876–5880. IEEE (2019)

    Google Scholar 

  24. Minaee, S., Azimi, E., Abdolrashidi, A.: Deep-sentiment: sentiment analysis using ensemble of CNN and Bi-LSTM models. arXiv:1904.04206 (2019)

  25. Nakayama, M., Wan, Y.: Is culture of origin associated with more expressions? An analysis of yelp reviews on japanese restaurants. Tour. Manag. 66, 329–338 (2018)

    Article  Google Scholar 

  26. Onishi, A., Natsume, K.: Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces. PloS One 9(4), e93045 (2014)

    Article  Google Scholar 

  27. Öztürk, N., Ayvaz, S.: Sentiment analysis on Twitter: a text mining approach to the syrian refugee crisis. Telemat. Inform. 35(1), 136–147 (2018)

    Article  Google Scholar 

  28. Peng, H., Ma, Y., Li, Y., Cambria, E.: Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowl. Based Syst. 148, 167–176 (2018)

    Article  Google Scholar 

  29. Poria, S., Hussain, A., Cambria, E.: Concept extraction from natural text for concept level text analysis. In: Multimodal Sentiment Analysis, pp. 79–84. Springer (2018)

    Google Scholar 

  30. Poria, S., Hussain, A., Cambria, E.: Sentic patterns: sentiment data flow analysis by means of dynamic linguistic patterns. In: Multimodal Sentiment Analysis, pp. 117–151. Springer (2018)

    Google Scholar 

  31. Rogers, A., Romanov, A., Rumshisky, A., Volkova, S., Gronas, M., Gribov, A.: RuSentiment: an enriched sentiment analysis dataset for social media in Russian. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 755–763 (2018)

    Google Scholar 

  32. Shuai, Q., Huang, Y., Jin, L., Pang, L.: Sentiment analysis on Chinese hotel reviews with Doc2Vec and classifiers. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1171–1174. IEEE (2018)

    Google Scholar 

  33. Sohangir, S., Wang, D., Pomeranets, A., Khoshgoftaar, T.M.: Big data: deep learning for financial sentiment analysis. J. Big Data 5(1), 3 (2018)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

    Kia Dashtipour

  2. DICEAM, University Mediterranea of Reggio Calabria, Via Graziella, Feo di Vito, 89060, Reggio Calabria, Italy

    Cosimo Ieracitano & Francesco Carlo Morabito

  3. Department of Electrical Engineering and Computing, Rochester Institute of Technology, Dubai, UAE

    Ali Raza

  4. School of Computing, Edinburgh Napier University, Merchiston Campus, Edinburgh, EH10 5DT, UK

    Amir Hussain

Authors
  1. Kia Dashtipour
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  2. Cosimo Ieracitano
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  3. Francesco Carlo Morabito
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  4. Ali Raza
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  5. Amir Hussain
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Corresponding author

Correspondence to Kia Dashtipour .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia and IIASS, Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataró, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni , Politecnico di Torino, Torino, Italy

    Eros Pasero

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Dashtipour, K., Ieracitano, C., Carlo Morabito, F., Raza, A., Hussain, A. (2021). An Ensemble Based Classification Approach for Persian Sentiment Analysis. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_20

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