Proceedings of EMSASW2018 - 4th Workshop on Sentic Computing, Sentiment Analysis, Opinion Mining, and Emotion Detection

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Copyright 2018 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Proceedings submitted to CEUR-WS.org Organizing Committee • Diego Reforgiato Recupero, University of Cagliari, Cagliari, Italy • Mauro Dragoni, Fondazione Bruno Kessler, Trento, Italy • Davide Buscaldi, Universite Paris 13, USPC, Paris, France • Mehwish Alam, STLab ISTC-CNR, Rome, Italy • Erik Cambria, Nanyang Technological University, Singapore Program Committee • Rada Mihalcea, University of North Texas (USA) • Ping Chen, University of Houston-Downtown (USA) • Yongzheng Zhang, LinkedIn Inc. (USA) • Giuseppe Di Fabbrizio, Amazon Inc. (USA) • Soujanya Poria, Nanyang Technological University (Singapore) • Yunqing Xia, Tsinghua University (China) • Rui Xia, Nanjing University of Science and Technology (China) • Jane Hsu, National Taiwan University (Taiwan) • Rafal Rzepka, Hokkaido University (Japan) • Amir Hussain, University of Stirling (UK) • Alexander Gelbukh, National Polytechnic Institute (Mexico) • Bjoern Schuller, Technical University of Munich (Germany) • Amitava Das, Samsung Research India (India) • Dipankar Das, National Institute of Technology (India) • Stefano Squartini, Marche Polytechnic University (Italy) • Cristina Bosco, University of Torino (Italy) • Paolo Rosso, Technical University of Valencia (Spain) As the Web rapidly evolves, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and the like. In recent years, this collective intelligence has spread to many di erent areas, with particular focus on elds related to everyday life such as commerce, tourism, education, and health, causing the size of the social Web to expand exponentially.

To identify the emotions (e.g. sentiment polarity, sadness, happiness, anger, irony, sarcasm, etc.) and the modality (e.g. doubt, certainty, obligation, liability, desire, etc.) expressed in this continuously growing content is critical to enable the correct interpretation of the opinions expressed or reported about social events, political movements, company strategies, marketing campaigns, product preferences, etc.

This has raised growing interest both within the scienti c community, by providing it with new research challenges, as well as in the business world, as applications such as marketing and nancial prediction would gain remarkable bene ts.

One of the main application tasks in this context is opinion mining [ 1 ], which is addressed by a signi cant number of Natural Language Processing techniques, e.g. for distinguishing objective from subjective statements [ 2 ], as well as for more ne-grained analysis of sentiment, such as polarity and emotions [ 9 ]. Recently, this has been extended to the detection of irony, humor, and other forms of gurative language [ 3 ]. In practice, this has led to the organisation of a series of shared tasks on sentiment analysis, including irony and gurative language detection (SemEval 2013, 2014, 2015, 2018), sometimes focused on the domain of nancial technology [25, 26, 27, 28] with the production of annotated data and development of running systems. A similar challenge for irony polarity detection has been proposed for the Italian language at SENTIPOLC1, indicating a growing interest about irony detection in the international NLP community. Similar challenges, not involving directly an irony detection task, but in which irony detection may prove useful, have been organized also for French (DEFT20152) and Spanish (TASS20153). In [ 10 ], the authors propose an algorithm for irony detection based on semantic similarity. Other studies such as [ 11, 12, 13, 14 ] consider features such as ambiguity, polarity etc.. However, the later also relies on decision trees.

However, existing solutions still have many limitations leaving the challenge of emotions and modality analysis still open. For example, there is the need for building/enriching semantic/cognitive resources for supporting emotion and modality recognition and analysis. Additionally, the joint treatment of modality and emotion is, computationally, trailing behind, and therefore the focus of ongoing, current research. Also, while we can produce rather robust deep semantic analysis of natural language, we still need to tune this analysis to1http://www.di.unito.it/~tutreeb/sentipolc-evalita14/ 2https://deft.limsi.fr/2015/ 3https://gplsi.dlsi.ua.es/sepln15/en/node/36 wards the processing of sentiment and modalities, which cannot be addressed by means of statistical models only, currently the prevailing approaches to sentiment analysis in NLP. The hybridization of NLP techniques with Semantic Web technologies is therefore a direction worth exploring, as recently shown in [ 4, 6, 7, 8, 5, 17, 21, 24, 23, 22 ].

This workshop intends to be a discussion forum gathering researchers and industries from Cognitive Linguistics, NLP, Machine Learning, Semantic Web, Big Data, and related areas for presenting their ideas on the relation between Semantic Web and the study of emotions and modalities.

Opinion mining, sentiment analysis, analysis of emotions and modalities are popular topics in the Natural Language Processing and Linguistics research elds. Regular workshops and challenges (shared tasks) on these themes are organised as co-located events with major conferences, such as IJCAI and ACL. Another recently organised related event is the MOMA (Models for Modality Annotation), a workshop held in London (April 2015) in conjunction with the International Conference on Computational Semantics (IWCS 2015). Our workshop intends to complement these events, focusing on the relation between these topics and the Semantic Web. [20] Aldo Gangemi, Harith Alani, Malvina Nissim, Erik Cambria, Diego Reforgiato Recupero, Vitaveska Lanfranchi, Tomi Kauppinen: Joint Proceedings of the 1th Workshop on Semantic Sentiment Analysis (SSA2014), and the Workshop on Social Media and Linked Data for Emergency Response (SMILE 2014) co-located with 11th European Semantic Web Conference (ESWC 2014), Crete, Greece, May 25th, 2014. CEUR Workshop Proceedings 1329, CEUR-WS.org 2015 [21] Diego Reforgiato Recupero, Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese, Daria Spampinato: A Semantic Web Based Core Engine to E ciently Perform Sentiment Analysis. ESWC (Satellite Events) 2014: 245-248 [22] Diego Reforgiato Recupero, Erik Cambria, Emanuele Di Rosa: Semantic Sentiment Analysis Challenge at ESWC2017. SemWebEval@ESWC 2017: 109-123 [23] Mauro Dragoni, Diego Reforgiato Recupero: Challenge on Fine-Grained

Sentiment Analysis Within ESWC2016. SemWebEval@ESWC 2016: 79-94 [24] Diego Reforgiato Recupero, Mauro Dragoni, Valentina Presutti: ESWC 15 Challenge on Concept-Level Sentiment Analysis. SemWebEval@ESWC 2015: 211-222 [25] Keith Cortis, Andr Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh and Brian Davis. 2017. Semeval-2017 task 5:Fine-grained sentiment analysis on nancial microblogs and news. In Proceedings of the 11th International Workshop on Semantic Evaluation . Association for Computational Linguistics, Vancouver, Canada, pages 517?533. (SemEval-2017) [26] Thomas Gaillat, Manel Zarrouk, Andr Freitas and Brian Davis (2018). The SSIX Corpus: A Trilingual Gold Standard Corpus for Sentiment Analysis in Financial Microblogs.11th edition of the Language Resources and Evaluation Conference, 7-12 May 2018, Miyazaki (Japan). (LREC 2018) [27] Amna Dridi, Mattia Atzeni, Diego Reforgiato Recupero: Bearish-Bullish

Sentiment Analysis on Financial Microblogs. EMSASW@ESWC 2017 [28] Mattia Atzeni, Amna Dridi, Diego Reforgiato Recupero: Fine-Grained Sentiment Analysis on Financial Microblogs and News Headlines. SemWebEval@ESWC 2017: 124-128 Spanish Corpus of Tweets for Marketing.

Mara Navas-Loro, Vctor Rodrguez Doncel, Idafen Santana-Prez, Alba FernndezIzquierdo and Alberto Snchez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Supervised Topic-Based Message Polarity Classi cation using Cognitive Computing.

Federico Ibba, Daniele Stefano Ferru and Diego Reforgiato Recupero . . . . . 11 On Finding the Relevant User Reviews for Advancing Conversational Faceted Search.

Eleftherios Dimitrakis, Konstantinos Sgontzos, Panagiotis Papadakos, Yannis Marketakis, Alexandros Papangelis, Yannis Stylianou and Yannis Tzitzikas 22 What does it mean to be a Wutburger? - A rst exploration. Manfred Klenner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 A Dataset for Detecting Irony in Hindi-English Code-Mixed Social Media Text.

Deepanshu Vijay, Aditya Bohra, Vinay Singh, Syed Sarfaraz Akhtar and Manish Shrivastava . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Leveraging Cognitive Computing for Gender and Emotion Detection. Andrea Corriga, Simone Cusimano, Francesca Malloci, Lodovica Marchesi and Diego Reforgiato Recupero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 In Search for Lost Emotions: Deep Learning for Opinion Taxonomy Induction.

Elena Melnikova, Emmanuelle Dusserre and Muntsa Padro . . . . . . . . . . . . . . 57 Detecting Truthful and Useful Consumer Reviews for Products using Opinion Mining.

Kalpana Algotar and Ajay Bansal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

References

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