Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE) is the IEEE ICDM workshop series on opinion mining. The term SENTIRE comes from the Latin feel and it is root of words such as sentiment and sensation. The main aim of SENTIRE is to explore the new frontiers of opinion mining and sentiment analysis by proposing novel techniques in fields such as AI, Semantic Web, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.

SENTIRE 2012
Due to many challenging research problems and a wide variety of practical applications, opinion mining and sentiment analysis have become very active research areas in the last decade. Our understanding and knowledge of the problem and its solution are still limited as natural language understanding techniques are still pretty weak. Most of current research in sentiment analysis, in fact, merely relies on machine learning algorithms. Such algorithms, despite most of them being very effective, produce no human understandable results such that we know little about how and why output values are obtained. All such approaches, moreover, rely on syntactical structure of text, which is far from the way human mind processes natural language. Next-generation opinion mining systems should employ techniques capable to better grasp the conceptual rules that govern sentiment and the clues that can convey these concepts from realisation to verbalisation in the human mind.

TOPICS
SENTIRE aims to provide an international forum for researchers in the field of opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of the workshop comprehends Web mining, AI, Semantic Web, information retrieval and natural language processing. In addition to paper presentations, an invited talk by Professor Rada Mihalcea will stress the interdisciplinary challenges of opinion mining and sentiment analysis. Topics of interest include but are not limited to:
•  Sentiment identification & classification
•  Opinion and sentiment summarisation & visualisation
•  Explicit & latent semantic analysis for sentiment mining
•  Opinion and sentiment search & retrieval
•  Time evolving opinion & sentiment analysis
•  Multi-modal sentiment analysis
•  Multi-domain & cross-domain evaluation
•  Multi-lingual sentiment analysis & re-use of knowledge bases
•  Knowledge base construction & integration with opinion analysis
•  Transfer learning of opinion & sentiment with knowledge bases
•  Sentiment corpora & annotation
•  Affective knowledge acquisition & representation
•  Sentiment topic detection & trend discovery
•  Social ranking
•  Social network analysis
•  Comparative opinion analysis
•  Opinion spam detection

TIMEFRAME
•  August 10th, 2012: Due date for workshop papers
•  October 1st, 2012: Notification of paper acceptance to authors
•  October 15th, 2012: Camera-ready deadline for accepted papers
•  December 10th, 2012: Workshop date

SUBMISSIONS
Papers submitted to this workshop must not have been accepted for publication elsewhere or be under review for another workshop, conference or journal. Papers can be either full research papers (10 pages) or short papers (6 pages) and must be formatted to IEEE Computer Society proceedings manuscript style.

PROCEEDINGS
Accepted papers will be published in IEEE ICDM workshop proceedings. Selected, expanded versions of papers presented at the workshop will be published in a follow-on Special Issue of Springer Cognitive Computation.

KEYNOTE
With more than 10,000 new videos posted online every day on social websites such as YouTube and Facebook, the internet is becoming an almost infinite source of information. One crucial challenge for the coming decade is to be able to harvest relevant information from this constant flow of multi-modal data. In this talk, the keynote speaker will introduce the task of multi-modal sentiment analysis, and present a method that integrates linguistic, audio, and visual features for the purpose of identifying sentiment in online videos. The invited speaker will first describe a novel dataset consisting of videos collected from the social media website YouTube and annotated for sentiment polarity. She will then show, through comparative experiments, that the joint use of visual, audio, and textual features greatly improves over the use of only one modality at a time. Finally, by running evaluations on datasets in English and Spanish, the keynote speaker will show that the method is portable and works equally well when applied to different languages.

INVITED SPEAKER
Rada Mihalcea is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas. Her research interests are in computational linguistics, with a focus on lexical semantics, graph-based algorithms for natural language processing, and multilingual natural language processing. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Research in Language in Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for the Conference of the Association for Computational Linguistics (2011), and the Conference on Empirical Methods in Natural Language Processing (2009). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009).

PROGRAM COMMITTEE
•  Alexandra Balahur, University of Alicante (Spain)
•  Sandra Baldassarri, University of Zaragoza (Spain)
•  Eva Cerezo, University of Zaragoza (Spain)
•  Praphul Chandra, HP Labs India (India)
•  Amitava Das, Norwegian University of Science and Technology (Norway)
•  Dipankar Das, Jadavpur University (India)
•  Sergio Decherchi, Italian Institute of Technology (Italy)
•  Rafael Del Hoyo, Aragon Institute of Technology (Spain)
•  Tariq Durrani, University of Strathclyde (UK)
•  Marco Grassi, Marche Polytechnic University (Italy)
•  Minlie Huang, Tsinghua University (China)
•  Isabelle Hupont, Aragon Institute of Technology (Spain)
•  Lillian Lee, Yahoo Labs (USA)
•  Saif Mohammad, National Research Council (Canada)
•  Muaz Niazi, Bahria University (Pakistan)
•  Paolo Rosso, Technical University of Valencia (Spain)
•  Bjoern Schuller, Technical University of Munich (Germany)
•  Stefano Squartini, Marche Polytechnic University (Italy)
•  Rui Xia, Nanjing University of Science and Technology (China)
•  Lei Zhang, University of Illinois at Chicago (USA)
•  Yongzheng Zhang, eBay Research Labs (USA)

ORGANIZERS
•  Erik Cambria, National University of Singapore (Singapore)
•  Bing Liu, University of Illinois at Chicago (USA)
•  Yunqing Xia, Tsinghua University (China)
•  Catherine Havasi, MIT Media Laboratory (USA)

 

SENTIRE 2011
Memory and data capacities double approximately every two years and, apparently, the Web is following the same rule. User-generated contents, in particular, are an ever-growing source of opinion and sentiments which are continuously spread worldwide through blogs, wikis, fora, chats and social networks. The distillation of knowledge from such sources is a key factor for applications in fields such as commerce, tourism, education and health, but the quantity and the nature of the contents they generate make it a very difficult task. Existing approaches to opinion mining and sentiment analysis can be grouped into four main categories: keyword spotting, in which text is classified according to the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion or opinion polarity; statistical methods, which calculate the valence of keywords and word co-occurrence frequencies on the base of a large training corpus; finally sentic computing, which uses affective ontologies and common sense reasoning tools for a concept-level analysis of natural language text.

TOPICS
•  Opinion & sentiment summarization and visualization
•  Explicit and latent semantic analysis for opinion & sentiment mining
•  Knowledge base construction and integration with opinion & sentiment analysis
•  Transfer learning of opinion & sentiment with knowledge bases
•  Time evolving opinion & sentiment analysis
•  Opinion & sentiment extraction and retrieval

PROGRAM
SESSION I
08:30-08:40  Opening Remarks (Erik Cambria)
08:40-09:30  Sentiment Analysis: A Discovery Challenge (Bing Liu)
09:35-10:00  STARLET: Multi-Document Summarization of Service and Product Reviews with Balanced Rating Distributions (Giuseppe Di Fabbrizio)

Coffee Break: 10:00-10:30

SESSION II
10:30-10:55  Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning (Philip Kegelmeyer)
11:00-11:25  Deriving Insights from National Happiness Indices (Daniel Archambault)
11:30-11:55  Multi-Aspect Sentiment Analysis with Topic Models (Myle Ott)
12:00-12:30  AQA: Aspect-based Opinion Question Answering (Samaneh Moghaddam)

Lunch: 12:30-14:00

SESSION III (Short Papers)
14:00-14:15  A Method for Improving Sentiment Classification Using Feature Highlighting (Lin Dai)
14:20-14:35  Machine Reading for Notion-Based Sentiment Mining (Roula Hobeica)
14:40-14:55  Mining Opinion Attributes From Texts using Multiple Kernel Learning (Aleksander Wawer)
15:00-15:15  Longitudinal Sales Responses with Online Reviews (Wenyin Liu)
15:20-15:35  Fine-Grained Opinion Mining Using Conditional Random Fields (Shabnam Shariaty)
15:40-16:00  Discourse Structure and Sentiment (Livia Polanyi)

Coffee Break: 16:00-16:30

SESSION IV
16:30-16:55  Detecting General Opinions from Customer Surveys (Evgeny Stepanov)
17:00-17:25  SES: Sentiment Elicitation System for Social Media Data (Yusheng Xie)
17:30-18:00  Concluding Remarks (Erik Cambria)

KEYNOTE
Sentiment analysis and opinion mining is the computational study of people’s opinions, evaluations, appraisals, attitudes, and emotions toward entities, issues, events, topics and their attributes. It has become a very active research area in natural language processing (NLP) and text mining due to many challenging research problems and a wide arrange of applications. The research has also spread from computer science to management science and social sciences. Although a large number of papers have been published, the pace of the progress has not been very fast due to the difficulty of natural language understanding. Many (if not most) existing papers do not make a lot of practical impact. One of the bottlenecks to the progress is the lack of effective mining algorithms that can discover complex expressions and domain common sense knowledge which are essential for determining sentiment orientations and for recognizing targets of opinions. In this talk, Professor Bing Liu will first introduce the sentiment analysis and opinion mining problem, and then discuss several sub-problems which need novel data mining algorithms that can work collaboratively with NLP techniques for their solution.

INVITED SPEAKER
Bing Liu is a professor of Computer Science at University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests include opinion mining and sentiment analysis, Web mining, and data mining. He has published extensively in leading conferences and journals in these fields. He has also written a textbook titled “Web Data Mining: Exploring Hyperlinks, Contents and Usage Data” published by Springer. The second edition of the book came out in July 2011. On professional services, Liu has served as program chairs of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), IEEE International Conference on Data Mining (ICDM), ACM Conference on Web Search and Data Mining (WSDM), SIAM Conference on Data Mining (SDM), ACM Conference on Information and Knowledge Management (CIKM), and Pacific Asia Conference on Data Mining (PAKDD). Additionally, he has also served as associate editors of IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Data Mining and Knowledge Discovery (DMKD), and SIGKDD Explorations, and is on the editorial boards of several other journals.

PROGRAM COMMITTEE
•  Alexandra Balahur, University of Alicante (Spain)
•  Sandra Baldassarri, University of Zaragoza (Spain)
•  Eva Cerezo, University of Zaragoza (Spain)
•  Praphul Chandra, HP Labs India (India)
•  Sergio Decherchi, Italian Institute of Technology (Italy)
•  Rafael Del Hoyo, Aragon Institute of Technology (Spain)
•  Tariq Durrani, University of Strathclyde (UK)
•  Marco Grassi, Marche Polytechnic University (Italy)
•  Isabelle Hupont, Aragon Institute of Technology (Spain)
•  Kenneth Kwok, National University of Singapore (Singapore)
•  Shixia Liu, Microsoft Research Asia (China)
•  Jinhu Lu, Chinese Academy of Sciences (China)
•  Saif Mohammad, National Research Council (Canada)
•  Muaz Niazi, University of Stirling (UK)
•  Paolo Rosso, Technical University of Valencia (Spain)
•  Bjoern Schuller, Technical University of Munich (Germany)
•  Stefano Squartini, Marche Polytechnic University (Italy)
•  Jose Troyano, University of Seville (Spain)
•  Rui Xia, Nanjing University of Science and Technology (China)
•  Lei Zhang, University of Illinois at Chicago (USA)
•  Chengqing Zong, Chinese Academy of Sciences (China)

ORGANIZERS
•  Erik Cambria, National University of Singapore (Singapore)
•  Yangqiu Song, Microsoft Research Asia (China)
•  Catherine Havasi, MIT Media Laboratory (USA)
•  Amir Hussain, University of Stirling (UK)