The Experts below are selected from a list of 1374 Experts worldwide ranked by ideXlab platform
Veselin Stoyanov - One of the best experts on this subject based on the ideXlab platform.
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semeval 2014 task 9 sentiment analysis in twitter
International Conference on Computational Linguistics, 2014Co-Authors: Sara Rosenthal, Alan Ritter, Preslav Nakov, Veselin StoyanovAbstract:We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) Livejournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.
Fanny Georges - One of the best experts on this subject based on the ideXlab platform.
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strat egies d autom ediation de l expression de soi au jeu des intersubjectivit es etude de la repr esentation de l usager dans Livejournal et touchgraph
arXiv: Computers and Society, 2017Co-Authors: Fanny GeorgesAbstract:Actor of its presentation and actor of its online representation, the diarist draws his diegetic existence by setting up a strategy of automediation. The Self-representation is a personal creation determined by the interface and the functionalities of the software. A pragmatic approach of the Self-representation in the Livejournal Blog and the Touchgraph Livejournal browser provides a way to observe the play between intimacy and intersubjectivity. The software leads the user from the lonely space of writing to the community space of publication.
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strategies d automediation de l expression de soi au jeu des intersubjectivites etude de la representation de l usager dans Livejournal et touchgraph
Hypertextes et hypermédias. Produits Outils et Méthodes H2PTM 2005 Créer Jouer Echanger : Expériences de réseaux, 2005Co-Authors: Fanny GeorgesAbstract:Acteur de sa presentation et de sa representation en ligne, le diariste dessine les contours de son existence diegetique en elaborant une strategie d'automediation. La representation de soi est une creation personnelle determinee par l'interface et les fonctionnalites du logiciel. Son usage manifeste des strategies disparates, de l'epanchement solipsiste a la collection de tests et plaisanteries destines a briguer le « top 10 » des blogs les plus visites. Le logiciel incite l'usager a sortir de l'espace solitaire de l'ecriture pour s'engager dans l'espace communautaire de la publication. Une analyse pragmatique des enjeux de la representation de soi dans le blog Livejournal et son navigateur Touchgraph est l'occasion d'observer comment l'intimite se prete au jeu des intersubjectivites.
Preslav Nakov - One of the best experts on this subject based on the ideXlab platform.
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SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter
2020Co-Authors: Boris Velichkov, Borislav Kapukaranov, Ivan Grozev, Jeni Karanesheva, Todor Mihaylov, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav NakovAbstract:Abstract We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twitter. We participated in subtask B, where the participating systems had to predict whether a Twitter message expresses positive, negative, or neutral sentiment. We trained an SVM classifier with a linear kernel using a variety of features. We used publicly available resources only, and thus our results should be easily replicable. Overall, our system is ranked 20th out of 50 submissions (by 44 teams) based on the average of the three 2014 evaluation data scores, with an F1-score of 63.62 on general tweets, 48.37 on sarcastic tweets, and 68.24 on Livejournal messages
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semeval 2014 task 9 sentiment analysis in twitter
International Conference on Computational Linguistics, 2014Co-Authors: Sara Rosenthal, Alan Ritter, Preslav Nakov, Veselin StoyanovAbstract:We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) Livejournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.
Olessia Koltsova - One of the best experts on this subject based on the ideXlab platform.
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communities of co commenting in the russian Livejournal and their topical coherence
Internet Research, 2016Co-Authors: Olessia Koltsova, Sergei Koltcov, Sergey I NikolenkoAbstract:– The paper addresses the problem of what drives the formation of latent discussion communities, if any, in the blogosphere: topical composition of posts or their authorship? The purpose of this paper is to contribute to the knowledge about structure of co-commenting. , – The research is based on a dataset of 17,386 full text posts written by top 2,000 Livejournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting, where posts are vertices. The Louvain algorithm is used to detect communities of co-commenting. Cosine similarity and topic modeling based on latent Dirichlet allocation are applied to study topical coherence within these communities. , – Bloggers unite into moderately manifest communities by commenting roughly the same sets of posts. The graph of co-commenting is sparse and connected by a minority of active non-top commenters. Communities are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics. , – The research has to be replicated on other datasets with more thorough hand coding to ensure the reliability of results and to reveal average proportions of topic-centered communities. , – Knowledge about factors around which co-commenting communities emerge, in particular clustered opinion leaders that often attract such communities, can be used by policy makers in marketing and/or political campaigning when individual leadership is not enough or not applicable. , – The research contributes to the social studies of online communities. It is the first study of communities based on co-commenting that combines examination of the content of commented posts and their topics.
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Livejournal libra the political blogosphere and voting preferences in russia in 2011 2012
New Media & Society, 2015Co-Authors: Olessia Koltsova, Andrey ShcherbakAbstract:This study explores relationship between the Internet and the Russian national election of 2011–2012. In contrast to other studies, we focus on the blogosphere as a political factor. Our conclusions are based on a study of the Livejournal blogging platform represented by a sample of political posts from the top 2000 bloggers for 13-week-long periods. Sampling from the population of about 180,000 posts was performed automatically with a topic modelling algorithm, while the analysis of the resulting 3690 texts was carried out manually by five coders. We found that the most influential Russian blogs perform the role of a media ‘stronghold’ of the political opposition. Moreover, we established a relationship between the weekly pre-election ratings of the opposition parties and presidential candidates and the indicators of political activity in the blogosphere. Our results cautiously suggest that political activity on the Internet is not simply an online projection of offline political activity: it can itself ...
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mapping the public agenda with topic modeling the case of the russian Livejournal
Policy & Internet, 2013Co-Authors: Olessia Koltsova, Sergei KoltcovAbstract:This article describes agendas as “packages” of topics of varying salience, set by the Russian Internet users on Russia’s leading blog platform Livejournal. The research involved modeling Livejournal’s topic structure, viewed as an important component of what is termed here selfgenerated public opinion. Topic modeling was performed automatically with the LDA algorithm, and complemented with hand labeling of topics. Data were collected by software created by the authors to generate a relational database storing all posts by the top 2,000 Livejournal users from three one-month periods: two during the Russian parliamentary and presidential elections 2011–2012, and one control period. We find that Livejournal top users share their attention evenly between “social/political” and “private/recreational” issues, the proportion being very stable. However, the substitution of diverse public affairs issues by the topics related to national street protests in the politicized periods compared to the control period was found both automatically and manually. The group of topics centered around social issues demonstrates the biggest volatility in terms of its composition and may serve as the foundation for monitoring self-generated public opinion by further application of sentiment/opinion mining methods to these topics.
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comment based discussion communities in the russian Livejournal and their topical coherence
Research Papers in Economics, 2013Co-Authors: Olessia Koltsova, Sergei Koltcov, Sergey I NikolenkoAbstract:We study the structure of online discussions in order to uncover latent communities of socially important debate. Our research reveals that discussion communities defined by mutual commenting in the Russian language blogosphere are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics. We have derived these conclusions from the dataset of 17386 full text posts written by top 2000 Livejournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting
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comment based discussion communities in the russian Livejournal and their topical
2013Co-Authors: Olessia Koltsova, Sergei Koltcov, Sergey I NikolenkoAbstract:We study the structure of online discussions in order to uncover latent communities of socially important debate. Our research reveals that discussion communities defined by mutual commenting in the Russian language blogosphere are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics. We have derived these conclusions from the dataset of 17386 full text posts written by top 2000 Livejournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting.
Sara Rosenthal - One of the best experts on this subject based on the ideXlab platform.
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semeval 2014 task 9 sentiment analysis in twitter
International Conference on Computational Linguistics, 2014Co-Authors: Sara Rosenthal, Alan Ritter, Preslav Nakov, Veselin StoyanovAbstract:We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) Livejournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.
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annotating agreement and disagreement in threaded discussion
Language Resources and Evaluation, 2012Co-Authors: Jacob Andreas, Sara Rosenthal, Kathleen R MckeownAbstract:We introduce a new corpus of sentence-level agreement and disagreement annotations over Livejournal and Wikipedia threads. This is the first agreement corpus to offer full-document annotations for threaded discussions. We provide a methodology for coding responses as well as an implemented tool with an interface that facilitates annotation of a specific response while viewing the full context of the thread. Both the results of an annotator questionnaire and high inter-annotator agreement statistics indicate that the annotations collected are of high quality.