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 different areas, with particular focus on fields 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 scientific community, by providing it with new research challenges, as well as in the business world, as applications such as marketing and financial prediction would gain remarkable benefits [12, 13, 14].
One of the main application tasks in this context is opinion mining , which is addressed by a significant number of Natural Language Processing techniques, e.g. for distinguishing objective from subjective statements , as well as for more fine-grained analysis of sentiment, such as polarity and emotions [8, 9, 10, 11]. Recently, this has been extended to the detection of irony, humor, and other forms of figurative language . In practice, this has led to the organisation of a series of shared tasks on sentiment analysis, including irony and figurative language detection (SemEval 2013, 2014, 2015), with the production of annotated data and development of running systems.
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 towards 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, 5, 6, 7, 8, 17].
Based on the lessons learned from the three previous editions [15, 16, 17], this year the scope of the workshop is a bit broader (although still focusing on a very specific domain) and accepted submissions will include abstracts and position papers in addition to full papers. The workshops main focus will be discussion rather than presentations, which are seen as seeds for boosting discussion topics, and an expected result will be a joint manifesto and a research roadmap that will provide the Semantic Web community with inspiring research challenges.
The Workshop will be connected to the ESWC 2018 Fine-Grained Sentiment Analysis Challenge. Both the Workshop and the Challenge can benefit from a Google Group, called Semantic Sentiment Analysis Initiative. Please post messages related to the Workshop under the discussion “ESWC 2018 Workshop on Sentic Computing, Sentiment Analysis, Opinion Mining, and Emotion Detection”
We have planned the following three actions to raise the impact of the workshop, the number of submissions and participants:
- We have included within the workshop chairs team key scientists that will help us advertising the workshop among their contacts (trying to get as many contributions as possible) and conducting the workshop at the conference (at least three of them will be present at ESWC2018);
- We have an informal agreement with an Italian company that sells Semantic Sentiment Analysis services to give the introductory speech at the workshop showing market opportunities and giving directions to fill the gap between research and market.
- We plan on bringing with us a robot (see Figure 1), showing it at the workshop indicating some applications of the Semantic Sentiment Analysis it can perform (e.g. it performs speech to text, and will be able to act or speak depending on what emotion/sentiment it is able to recognize). We hope this can further open talking points towards future applications within the robotic domain.
 Bo, P., and Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval , 2 (1-2), 1-135.
 Wiebe, J., and Ellen, R. (2005). Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. Computational Linguistics and Intelligent Text Processing 6th International Conference, CICLing (pp. 486-497). Mexico City: Springer.
 Paula, C., Sarmento, L., Silva, M. J., and de Oliveira, E. (2009). Clues for detecting irony in user-generated contents: oh…!! it’s so easy;-). Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion (pp. 53-56). ACM.
 Reforgiato Recupero, D., Presutti, V., Consoli, S., and Gangemi, A. (2014). Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation , 1-15.
 Saif, H., He, Y., and Alani, H. (2012). Semantic sentiment analysis of Twitter. 11th International Semantic Web Conference (ISWC 2012) (pp. 508-524). Springer.
 Gangemi, A., Presutti, V., and Reforgiato Recupero, D. (2014). Frame- based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence , 9 (1), 20-30.
 Cambria, E., and Hussain, A. (2012). Sentic Computing: Techniques, Tools, and Applications. Springer.
 Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Chicago: Morgan & Claypool Publishers.
 Toropova, V., A., (2014). Irony detection based on semantic similarity. SPIIRAS Proceedings, Vol. 1.
11. Reyes, A. and Rosso, P. and Buscaldi, D (2012). From humor recognition to irony detection: The figurative language of social media. Journal of Data & Knowledge Engineering, Vol. 74.
 Reyes, A. and Rosso, P. and Veale, T., (2013). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation
 Barbieri, F. and Saggion, H., (2014). Automatic Detection of Irony and Humour in Twitter. International Conference on Computational Creativity.
 Dragoni, M. (2017). A Three-Phase Approach for Exploiting Opinion Mining in Computational Advertising. IEEE Intelligent Systems 32(3): 21-27 (2017)
 Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C. (2015). Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis. Cognitive Computation 7(2): 186-197 (2015)
 Dragoni, M., Petrucci, G. (2017). A Neural Word Embeddings Approach For Multi-Domain Sentiment Analysis. IEEE Transactions on Affective Computing 8(4): 457-470 (2017)
 Mauro Dragoni, Diego Reforgiato Recupero: Proceedings of the 3rd International Workshop at ESWC on Emotions, Modality, Sentiment Analysis and the Semantic Web co-located with 14th ESWC 2017, Portroz, Slovenia, May 28, 2017. CEUR Workshop Proceedings 1874, CEUR-WS.org 2017
 Mauro Dragoni, Diego Reforgiato Recupero, Kerstin Denecke, Yihan Deng, Thierry Declerck: Joint Proceed- ings of the 2th Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web and the 1st International Workshop on Extraction and Processing of Rich Semantics from Medical Texts co-located with
 ESWC 2016, Heraklion, Greece, May 29, 2016. CEUR Workshop Proceedings 1613, CEUR-WS.org 2016
20. Aldo Gangemi, Harith Alani, Malvina Nissim, Erik Cambria, Diego Reforgiato Recupero, Vitaveska Lan- franchi, 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