Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.

Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. The output of the stance detection procedure is usually from this set: Favor, Against, None. In this tutorial, we will define the core concepts and research problems related to stance detection, present historical and contemporary approaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and application areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.

Sentiment analysis of Twitter data is a well studied area, however, there is a need for exploring the effectiveness of real-time approaches on small data sets that only include popular and targeted tweets. In this paper, we have employed several sentiment analysis techniques by using dynamic dictionaries and models, and performed some experiments on limited but relevant datasets to understand the popularity of some terms and the opinion of users about them. The results of our experiments are promising.

We describe a system for real-time detection of security and crisis events from online news in three Balkan languages: Turkish, Romanian and Bulgarian.The system classifies the events according to a finegrained event type set.It extracts structured information from news reports, by using a blend of keyword matching and finite-state grammars for entity recognition.We apply a multilingual methodology for the development of the system's language resources, based on adaptation of language-independent grammars and on weakly-supervised learning of lexical resources.Detailed performance evaluation proves that the approach is effective in developing real-world semantic processing applications for relatively less-resourced languages.

This paper describes an enhanced monitoring and forecast system for the electrical power generated from wind in Turkey. Wind power plant (WPP) owners, transmission system operator, distribution system operators, and renewable energy experts are the shareholders of this system. The developed monitoring and forecast system is composed of wind electricity analyzers and reference wind masts, one for each WPP, a Wind-Electric Power Monitoring and Forecast Center (WPMFC) equipped with database, data processing, and application servers, and the associated monitoring and forecast software. All shareholders can communicate with the developed WPMFC in order to retrieve preprocessed wind-electric power forecast data and power quality data, through the IP network according to their authorization level. The electrical and meteorological raw data can also be retrieved for postprocessing. The developed system is based on global positioning system (GPS) synchronized, real-time field measurements taken from each WPP, in addition to the use of mesoscale numerical weather prediction models. Instantaneous electrical and meteorological quantities of each WPP can be monitored continuously through the WPMFC. The forecast system employs physical, statistical, and hybrid models combining both approaches. Some sample monitoring and forecast results are given in the paper to illustrate the benefits and abilities of the developed system.

Video texts are known to constitute an important source of information for semantic summaries of video archives. In this study, we propose a fully automated architecture for semantic annotation and later retrieval of Turkish news videos based on the corresponding video texts. At the core of the architecture is a named entity recognizer, the output of which on video texts is used as semantic annotations for the corresponding videos. The architecture also comprises components for news story segmentation, sliding text recognition, and video retrieval in addition to a news video database. The news story segmentation module makes use of the audio waveforms of the raw video files to detect the boundaries of individual news stories. The sliding text recognizer is then executed on the video segments corresponding to these news stories to extract their texts. The texts are then fed into the named entity recognizer for Turkish news texts to extract the named entities which are to be used as semantic annotations or index terms for the retrieval of these news videos. Finally, the retrieval interface of the overall architecture enables access to the annotated videos and video segments through boolean queries formed by using the previously extracted named entities. This study is significant for its proposing the first fully automated architecture for the semantic annotation and retrieval of Turkish news video archives.

The size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing the domain and thereby provides an evidence for the general applicability of the model. The model is accompanied by an automatic multimedia annotation scheme which makes use of information extraction techniques on the corresponding multimedia texts.