EMNLP 2017 WASSA 2017
8th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
To be held in conjuntion with the EMNLP 2017 Conference

European Commission Joint Research Centre

Prof. Dr. Iryna Gurevych - Technical University of Darmstadt

Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental Questions

Mining arguments from natural language texts, parsing argumentative structures, and assessing argument quality are among the recent challenges tackled in computational argumentation. While advanced deep learning models provide state-of-the-art performance in many of these tasks, much attention is also paid to the underlying fundamental questions. How are arguments expressed in natural language across genres and domains? What is the essence of an argument's claim? Can we reliably annotate convincingness of an argument? How can we approach logic and common-sense reasoning in argumentation? This talk highlights some recent advances in computational argumentation and shows why researchers must be both "surfers" and "scuba divers".

Dr. Iryna Gurevych is professor of computer science at TU Darmstadt, where she heads the UKP Lab and the Research Training Group "Adaptive Preparation of Information from Heterogeneous Sources" (AIPHES). She has a broad range of research interests in natural language processing, with a focus on computational argumentation, computational lexical semantics, semantic information management, and discourse and dialogue processing. She has co-founded and co-organized the workshop series "Collaboratively Constructed Semantic Resources and their Applications to NLP", "Argument Mining" and several research events on innovative applications of NLP to education, social sciences and humanities.

Dr. Viktor Pekar - University of Birmingham

Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media

Consumer spending is a vital macroeconomic indicator. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.

Dr. Viktor Pekar is a Research Fellow at the School of Computer Science and the Business School at the University of Birmingham, UK. His main research interests and experience are methods for semantic processing of text, sentiment analysis, and data analysis. His current work investigates possibilities to extract predictive signals about different socio-economic phenomena such a consumer behaviour from social media data.

Aditya Joshi

Detecting Sarcasm Using Different Forms Of Incongruity

Sarcasm is a form of verbal irony that is intended to express contempt or ridicule. Often quoted as a challenge to sentiment analysis, sarcasm involves use of words of positive or no polarity to convey negative sentiment. Incongruity has been observed to be at the heart of sarcasm understanding in humans. Our work in sarcasm detection identifies different forms of incongruity and employs different machine learning techniques to capture them. This talk will describe the approach, datasets and challenges in sarcasm detection using different forms of incongruity. We identify two forms of incongruity: incongruity which can be understood based on the target text and common background knowledge, and incongruity which can be understood based on the target text and additional, specific context. The former involves use of sentiment-based features, word embeddings, and topic models. The latter involves creation of author's historical context based on their historical data, and creation of conversational context for sarcasm detection of dialogue.

Aditya Joshi is a PhD candidate at IITB-Monash Research Academy, a joint PhD programme between Indian Institute of Technology Bombay, India and Monash University, Australia. He holds a MTech degree in Computer Science and Engineering from Indian Institute of Technology Bombay, India. His research interests in natural language processing span novel applications (drunk texting prediction, political orientation prediction, news headline translation, etc.) and foundational studies (computational sarcasm, sentiment analysis for Indian languages, etc.). His publications include a book chapter on sentiment lexical resources and a recent literature survey on 'Computational Sarcasm' in ACM Computing Surveys. He, along with Prof. Pushpak Bhattacharyya, are also scheduled to conduct a pre-conference tutorial on Computational Sarcasm at EMNLP 2017.

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