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Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

Published: 27 January 2021 Publication History

Abstract

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.

References

[1]
Seokjin Ahn, Stéfan V. Couture, Alfredo Cuzzocrea, Kevin Dam, Giorgio Mario Grasso, Carson K. Leung, Kaleigh L. McCormick, and Bryan H. Wodi. 2019. A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments. In 2019 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2019, New Orleans, LA, USA, June 23-26, 2019. IEEE, 1--6.
[2]
Samah Aloufi and Abdulmotaleb El Saddik. 2018. Sentiment identification in football-specific tweets. IEEE Access 6 (2018), 78609--78621.
[3]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv:1409.0473 [cs.CL]
[4]
David Bamman and Noah A Smith. 2015. Contextualized sarcasm detection on twitter. In Ninth International AAAI Conference on Web and Social Media.
[5]
Francesco Barbieri, Francesco Ronzano, and Horacio Saggion. 2014. Italian irony detection in twitter: a first approach. In The First Italian Conference on Computational Linguistics CLiC-it, Vol. 28.
[6]
Mondher Bouazizi and Tomoaki Ohtsuki. 2018. Multi-class sentiment analysis in Twitter: What if classification is not the answer. IEEE Access 6 (2018), 64486--64502.
[7]
Harold Edgeworth Butler et al. 1953. The Instituto Oratoria of Quintilian: With an English Translation. Vol. 1. W. Heinemann.
[8]
Alfredo Cuzzocrea. 2006. Improving range-sum query evaluation on data cubes via polynomial approximation. Data Knowl. Eng. 56, 2 (2006), 85--121.
[9]
Alfredo Cuzzocrea and Sharma Chakravarthy. 2010. Event-based lossy compression for effective and efficient OLAP over data streams. Data Knowl. Eng. 69, 7 (2010), 678--708.
[10]
Alfredo Cuzzocrea, Filippo Furfaro, and Domenico Saccà. 2003. Hand-OLAP: A System for Delivering OLAP Services on Handheld Devices. In 6th International Symposium on Autonomous Decentralized Systems (ISADS 2003), 9-11 April 2003, Pisa, Italy. IEEE Computer Society, 80--87.
[11]
Alfredo Cuzzocrea and Ugo Matrangolo. 2004. Analytical Synopses for Approximate Query Answering in OLAP Environments. In Database and Expert Systems Applications, 15th International Conference, DEXA 2004 Zaragoza, Spain, August 30-September 3, 2004, Proceedings (Lecture Notes in Computer Science, Vol. 3180), Fernando Galindo, Makoto Takizawa, and Roland Traunmüller (Eds.). Springer, 359--370.
[12]
Alfredo Cuzzocrea, Domenico Saccà, and Paolo Serafino. 2006. A Hierarchy-Driven Compression Technique for Advanced OLAP Visualization of Multidimensional Data Cubes. In Data Warehousing and Knowledge Discovery, 8th International Conference, DaWaK 2006, Krakow, Poland, September 4-8, 2006, Proceedings (Lecture Notes in Computer Science, Vol. 4081), A Min Tjoa and Juan Trujillo (Eds.). Springer, 106--119.
[13]
Alfredo Cuzzocrea and Paolo Serafino. 2009. LCS-Hist: taming massive high-dimensional data cube compression. In EDBT 2009, 12th International Conference on Extending Database Technology, Saint Petersburg, Russia, March 24-26, 2009, Proceedings (ACM International Conference Proceeding Series, Vol. 360), Martin L. Kersten, Boris Novikov, Jens Teubner, Vladimir Polutin, and Stefan Manegold (Eds.). ACM, 768--779.
[14]
Ernesto D'Avanzo, Giovanni Pilato, and Miltiadis Lytras. 2017. Using Twitter sentiment and emotions analysis of Google Trends for decisions making. Program (2017).
[15]
Mattia Antonino Di Gangi, Giosue' Lo Bosco, and Pilato Giovanni. 2019. Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection. Natural Language Engineering 25 (2019), 257--285.
[16]
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 1615--1625.
[17]
F. A. Gers, J. Schmidhuber, and F. Cummins. 1999. Learning to forget: continual prediction with LSTM. In 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), Vol. 2. 850--855 vol.2.
[18]
Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis. 161--169.
[19]
Valentino Giudice. 2018. Aspie96 at IronITA (EVALITA 2018): Irony Detection in Italian Tweets with Character-Level Convolutional RNN. Proceedings of the 6th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'18) (2018), 160--165.
[20]
Roberto González-Ibánez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in Twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 581--586.
[21]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.
[22]
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018. Learning Word Vectors for 157 Languages. CoRR abs/1802.06893 (2018). arXiv:1802.06893
[23]
Giosué Lo Bosco, Giovanni Pilato, and Daniele Schicchi. 2018. A Neural Network model for the Evaluation of Text Complexity in Italian Language: a Representation Point of View. Procedia computer science 145 (2018), 464--470.
[24]
Giosué Lo Bosco, Giovanni Pilato, and Daniele Schicchi. 2018. A Recurrent Deep Neural Network Model to measure Sentence Complexity for the Italian Language. In Proceedings of the sixth International Workshop on Artificial Intelligence and Cognition.
[25]
G Lo Bosco, G Pilato, and D Schicchi. 2018. A sentence based system for measuring syntax complexity using a recurrent deep neural network. In 2nd Workshop on Natural Language for Artificial Intelligence, NL4AI 2018, Vol. 2244. CEUR-WS, 95--101.
[26]
Diana G Maynard and Mark A Greenwood. 2014. Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In LREC 2014 Proceedings. ELRA.
[27]
Shereen Oraby, Vrindavan Harrison, Lena Reed, Ernesto Hernandez, Ellen Riloff, and Marilyn Walker. 2017. Creating and characterizing a diverse corpus of sarcasm in dialogue. arXiv preprint arXiv:1709.05404 (2017).
[28]
Ping-Feng Pai and Chia-Hsin Liu. 2018. Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access 6 (2018), 57655--57662.
[29]
Antonio Reyes and Paolo Rosso. 2014. On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowledge and Information Systems 40, 3 (2014), 595--614.
[30]
D. Schicchi, G. Pilato, and G. Lo Bosco. 2020. Attention-based Model for Evaluating the Complexity of Sentences in English Language. In 20th ieee mediterranean eletrotechnical conference. in press.
[31]
D. Schicchi, G. Pilato, and G. Lo Bosco. 2020. Deep Neural Attention-Based Model for the Evaluation of Italian Sentences Complexity. In 2020 IEEE 14th International Conference on Semantic Computing (ICSC). 253--256.
[32]
Le Hoang Son, Akshi Kumar, Saurabh Raj Sangwan, Anshika Arora, Anand Nayyar, and Mohamed Abdel-Basset. 2019. Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network. IEEE Access 7 (2019), 23319--23328.
[33]
Diego Terrana, Agnese Augello, and Giovanni Pilato. 2014. Automatic unsupervised polarity detection on a twitter data stream. In 2014 IEEE International Conference on Semantic Computing. IEEE, 128--134.
[34]
Diego Terrana, Agnese Augello, and Giovanni Pilato. 2014. Facebook users relationships analysis based on sentiment classification. In 2014 IEEE International Conference on Semantic Computing. IEEE, 290--296.
[35]
Oren Tsur, Dmitry Davidov, and Ari Rappoport. 2010. ICWSM---a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In fourth international AAAI conference on weblogs and social media.
[36]
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California.

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  • (2023)A Novel Deep Learning Language Model with Hybrid-GFX Embedding and Hyperband Search for Opinion AnalysisSN Computer Science10.1007/s42979-023-02236-84:6Online publication date: 29-Sep-2023
  • (2022)Sarcasm Detection in English Text using Tweets and Headlines2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)10.1109/DISCOVER55800.2022.9974808(192-196)Online publication date: 14-Oct-2022
  • (2022)bNaming: An Intelligent Application to Assist Brand Names DefinitionInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_6(75-89)Online publication date: 20-Nov-2022
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  1. Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

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      iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
      November 2020
      492 pages
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      • Johannes Kepler University, Linz, Austria

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      Published: 27 January 2021

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      Author Tags

      1. deep learning
      2. irony detection
      3. natural language processing
      4. sarcasm detection

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      View all
      • (2023)A Novel Deep Learning Language Model with Hybrid-GFX Embedding and Hyperband Search for Opinion AnalysisSN Computer Science10.1007/s42979-023-02236-84:6Online publication date: 29-Sep-2023
      • (2022)Sarcasm Detection in English Text using Tweets and Headlines2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)10.1109/DISCOVER55800.2022.9974808(192-196)Online publication date: 14-Oct-2022
      • (2022)bNaming: An Intelligent Application to Assist Brand Names DefinitionInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_6(75-89)Online publication date: 20-Nov-2022
      • (2021)A Novel Approach for Supporting Italian Satire Detection Through Deep LearningFlexible Query Answering Systems10.1007/978-3-030-86967-0_13(170-181)Online publication date: 16-Sep-2021

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