In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results from the Twitter public sentiment indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx. The BREXIT Political party sentiment for the “No Deal” was indicative of short-term dynamics on the GBP/EUR Fx at a marginal rate. Our test concluded that there was no causality on the FTSE 100.
{"title":"The Political Power of Twitter","authors":"J. Usher, Pierpaolo Dondio, Lucía Morales","doi":"10.1145/3350546.3352541","DOIUrl":"https://doi.org/10.1145/3350546.3352541","url":null,"abstract":"In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results from the Twitter public sentiment indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx. The BREXIT Political party sentiment for the “No Deal” was indicative of short-term dynamics on the GBP/EUR Fx at a marginal rate. Our test concluded that there was no causality on the FTSE 100.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74683556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE/WIC/ACM International Conference on Web Intelligence","authors":"","doi":"10.1145/3350546","DOIUrl":"https://doi.org/10.1145/3350546","url":null,"abstract":"","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80920476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.
{"title":"Detection of Valid Sentiment-Target Pairs in Online Product Reviews and News Media Articles","authors":"Svitlana Vakulenko, A. Weichselbraun, A. Scharl","doi":"10.1109/WI.2016.0024","DOIUrl":"https://doi.org/10.1109/WI.2016.0024","url":null,"abstract":"This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify features that optimize predictive performance. Our experimental evaluation draws upon two corpora of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"190 12 1","pages":"97-104"},"PeriodicalIF":0.0,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87333597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many advanced traveler information systems (ATIS) provide travel solutions that are limited, by technical obstructions or by design, in terms of geographical coverage, transport services, and/or travel modes. However, using existing ATIS in an integrated manner can broaden the coverage of travel solutions, while preserving the advantages of each system. This paper presents an approach to exploit web resources such as ATIS, data sources and services to construct travel solutions. More precisely, it focuses on itinerary formulation and discovery of resources relevant to each itinerary. We propose a semantic model for describing and interlinking geographic entities in relation to web resources, creating a graph of related entities. Itinerary formulation and resource discovery are achieved via exploring the graph. To this end, we propose an adapted version of multi-agent A* algorithm.
{"title":"A Distributed Approach to Constructing Travel Solutions by Exploiting Web Resources","authors":"O. Kem, Flavien Balbo, Antoine Zimmermann","doi":"10.1109/WI.2016.0128","DOIUrl":"https://doi.org/10.1109/WI.2016.0128","url":null,"abstract":"Many advanced traveler information systems (ATIS) provide travel solutions that are limited, by technical obstructions or by design, in terms of geographical coverage, transport services, and/or travel modes. However, using existing ATIS in an integrated manner can broaden the coverage of travel solutions, while preserving the advantages of each system. This paper presents an approach to exploit web resources such as ATIS, data sources and services to construct travel solutions. More precisely, it focuses on itinerary formulation and discovery of resources relevant to each itinerary. We propose a semantic model for describing and interlinking geographic entities in relation to web resources, creating a graph of related entities. Itinerary formulation and resource discovery are achieved via exploring the graph. To this end, we propose an adapted version of multi-agent A* algorithm.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"117 1","pages":"713-716"},"PeriodicalIF":0.0,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79263263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amel Ben Othmane, A. Tettamanzi, S. Villata, Nhan Le Thanh
In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender system in a realistic scenario, to understand how groups of agents behave in a social network compared to individual agents. Results show that agents within a social network have better collective performance than individual ones. The utility and the satisfaction of agents is increased by the exchange of messages when executing intentions.
{"title":"A Multi-context BDI Recommender System: From Theory to Simulation","authors":"Amel Ben Othmane, A. Tettamanzi, S. Villata, Nhan Le Thanh","doi":"10.1109/WI.2016.0104","DOIUrl":"https://doi.org/10.1109/WI.2016.0104","url":null,"abstract":"In this paper, a simulation of a multi-agent recommender system is presented and developed in the NetLogo platform. The specification of this recommender system is based on the well known Belief-Desire-Intention agent architecture applied to multi-context systems, extended with contexts for additional reasoning abilities, especially social ones. The main goal of this simulation study is, besides illustrating the usefulness and feasibility of our agent-based recommender system in a realistic scenario, to understand how groups of agents behave in a social network compared to individual agents. Results show that agents within a social network have better collective performance than individual ones. The utility and the satisfaction of agents is increased by the exchange of messages when executing intentions.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"160 1","pages":"602-605"},"PeriodicalIF":0.0,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85847218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, uncovering the temporal patterns and trends in their activity is of prime interest to detect their current expertize. These temporal variations have long remained unexplored in question-answer sites while detecting them enables us to improve tasks such as: question routing, expert recommending and community life-cycle management. In this paper, we propose a generative model of such a community and its dynamics, and we perform experiments with real-world data extracted from the StackOverflow website to confirm the effectiveness of our model to study the users' behaviors and topics dynamics.
{"title":"Joint Model of Topics, Expertises, Activities and Trends for Question Answering Web Applications","authors":"Zide Meng, Fabien L. Gandon, C. Faron-Zucker","doi":"10.1109/WI.2016.0049","DOIUrl":"https://doi.org/10.1109/WI.2016.0049","url":null,"abstract":"Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, uncovering the temporal patterns and trends in their activity is of prime interest to detect their current expertize. These temporal variations have long remained unexplored in question-answer sites while detecting them enables us to improve tasks such as: question routing, expert recommending and community life-cycle management. In this paper, we propose a generative model of such a community and its dynamics, and we perform experiments with real-world data extracted from the StackOverflow website to confirm the effectiveness of our model to study the users' behaviors and topics dynamics.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"34 1","pages":"296-303"},"PeriodicalIF":0.0,"publicationDate":"2016-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84811669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A software reference architecture specifies a generic architectural solution for the development of specific software architectures. It includes common components to all software architectures and their relationships, a common vocabulary, a mapping methodology for realizing a specific architecture and good design practices. Software agents represent an evolution of traditional software, having the ability to control their own behavior and acting with autonomy. Typically, software agents act reactively, where actions and perceptions are predefined at design time, or in a deliberative way, where the corresponding action for a given perception is found at run time through a process of reasoning. However, to perform better, software agents should act using both forms of behavior with learning abilities in a hybrid way. In this paper, a reference architecture that specifies a generic architectural solution for the development of specific architectures of hybrid learning agents is presented. An example of realization of this architecture in the network intrusion domain is also presented.
{"title":"A Reference Architecture of a Hybrid Learning Agent","authors":"Adriana Leite, R. Girardi","doi":"10.1109/WI.2016.0066","DOIUrl":"https://doi.org/10.1109/WI.2016.0066","url":null,"abstract":"A software reference architecture specifies a generic architectural solution for the development of specific software architectures. It includes common components to all software architectures and their relationships, a common vocabulary, a mapping methodology for realizing a specific architecture and good design practices. Software agents represent an evolution of traditional software, having the ability to control their own behavior and acting with autonomy. Typically, software agents act reactively, where actions and perceptions are predefined at design time, or in a deliberative way, where the corresponding action for a given perception is found at run time through a process of reasoning. However, to perform better, software agents should act using both forms of behavior with learning abilities in a hybrid way. In this paper, a reference architecture that specifies a generic architectural solution for the development of specific architectures of hybrid learning agents is presented. An example of realization of this architecture in the network intrusion domain is also presented.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"13 1","pages":"421-424"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73492928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a method to generate effective query suggestions aiming to help struggling search, where users experience difficulty in locating information that is relevant to their information need in the search session. The core is identifying struggling component of an on-going struggling session and mining the effective representations of it. The struggling component is the semantic component of information need for which the user struggled to find an effective representation during the struggling session. The proposed method identifies the struggling component of given on-going struggling session and mines the sessions containing the identified struggling component from a query log to build a struggling flow graph. The struggling flow graph records users' reformulation behaviors for the terms of the struggling component, through struggling flow graph we can mine effective representations of the struggling component. The experimental results demonstrate that the proposed method outperforms the baseline methods when it can use two or more queries in a struggling session.
{"title":"Query Suggestion for Struggling Search by Struggling Flow Graph","authors":"Zebang Chen, Takehiro Yamamoto, Katsumi Tanaka","doi":"10.1109/WI.2016.0040","DOIUrl":"https://doi.org/10.1109/WI.2016.0040","url":null,"abstract":"We propose a method to generate effective query suggestions aiming to help struggling search, where users experience difficulty in locating information that is relevant to their information need in the search session. The core is identifying struggling component of an on-going struggling session and mining the effective representations of it. The struggling component is the semantic component of information need for which the user struggled to find an effective representation during the struggling session. The proposed method identifies the struggling component of given on-going struggling session and mines the sessions containing the identified struggling component from a query log to build a struggling flow graph. The struggling flow graph records users' reformulation behaviors for the terms of the struggling component, through struggling flow graph we can mine effective representations of the struggling component. The experimental results demonstrate that the proposed method outperforms the baseline methods when it can use two or more queries in a struggling session.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"55 1","pages":"224-231"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81275911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quanzhi Li, Sameena Shah, Rui Fang, Armineh Nourbakhsh, Xiaomo Liu
Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment-specific word embeddings (SSWE) and a weighted text feature model (WTFM). WTFM produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. Compared to other tweet sentiment feature generation approaches, WTFM is easy to build, simple, yet effective. Experiments show that the proposed approach outperforms the two state-of-the-art tweet sentiment classification methods, SSWE and National Research Council Canada's (NRC) model.
{"title":"Tweet Sentiment Analysis by Incorporating Sentiment-Specific Word Embedding and Weighted Text Features","authors":"Quanzhi Li, Sameena Shah, Rui Fang, Armineh Nourbakhsh, Xiaomo Liu","doi":"10.1109/WI.2016.0097","DOIUrl":"https://doi.org/10.1109/WI.2016.0097","url":null,"abstract":"Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment-specific word embeddings (SSWE) and a weighted text feature model (WTFM). WTFM produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. Compared to other tweet sentiment feature generation approaches, WTFM is easy to build, simple, yet effective. Experiments show that the proposed approach outperforms the two state-of-the-art tweet sentiment classification methods, SSWE and National Research Council Canada's (NRC) model.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"11 1","pages":"568-571"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84381163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years Semantic Web technologies and the Linked Data paradigm have allowed the emergence of large interlinked knowledge bases as Linked datasets. These databases contain information that associates Web entities (called resources) with a well-defined semantics that specifies how these entities should be interpreted. A way to perform this task is through a class assignment process where resources are identified as members of certain classes described in ontologies. In order to improve the quality of the "meaning" of the data contained in Linked datasets a key challenge in the Linked Data community is to detect, assess and eventually fix wrong class assignments. In this sense, this work proposes an interpretation for adequate class assignments considering three quality dimensions from a semantic perspective: redundancy, consistency and accuracy. For each dimension, a formal definition is presented, then applied to class assignments and finally used as guideline to show how quality metrics and data curation strategies can be defined.
{"title":"Adequate Class Assignments on Linked Data","authors":"L. Mendoza, A. Díaz","doi":"10.1109/WI.2016.0077","DOIUrl":"https://doi.org/10.1109/WI.2016.0077","url":null,"abstract":"In recent years Semantic Web technologies and the Linked Data paradigm have allowed the emergence of large interlinked knowledge bases as Linked datasets. These databases contain information that associates Web entities (called resources) with a well-defined semantics that specifies how these entities should be interpreted. A way to perform this task is through a class assignment process where resources are identified as members of certain classes described in ontologies. In order to improve the quality of the \"meaning\" of the data contained in Linked datasets a key challenge in the Linked Data community is to detect, assess and eventually fix wrong class assignments. In this sense, this work proposes an interpretation for adequate class assignments considering three quality dimensions from a semantic perspective: redundancy, consistency and accuracy. For each dimension, a formal definition is presented, then applied to class assignments and finally used as guideline to show how quality metrics and data curation strategies can be defined.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"12 1","pages":"469-472"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84497960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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