Definition and Measures of an Opinion Model for Mining Forums

Stavrianou, Anna; Velcin, Julien; Chauchat, Jean-Hugues

doi:10.1109/asonam.2009.34

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…However, this is difficult to do in an automatic way and as we experimented, the text quotation seems not to be sufficient on its own to recognise the celebrity. In order to improve the interesting post recognition we are thinking to use an opinion extraction model [18] or to measure the average length of a thread initiated by a user, i.e., automatically determine whether a post is interesting for the community or not.…”

Section: Discussion and Perspectives A) A Celebrity Is Not Just A mentioning

confidence: 99%

Extracting Celebrities from Online Discussions

Forestier¹,

Velcin²,

Stavrianou

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Online discussions became increasingly widespread with the Web 2.0: no matter the distance, whether you know the person or not, you can discuss and exchange ideas with people all over the world through forums, blogs, and newsgroups. The news websites have extensively used forums in order to encourage the reader being a real participant in the information media. This paper aims at automatically extracting the celebrities from such discussions. We propose certain meta-criteria and we provide an evaluation on a dataset of 35,175 posts written by 14,443 users. The results show that one of the proposed meta-criteria succeeds in extracting celebrities and allows for further improvements. I. INTRODUCTIONThe Web 2.0 has introduced an incredibly simple way of interaction: regardless the distance or whether you know the person or not, you can discuss with people from all over the world through the Web 2.0 applications (blogs, e-mails, dedicated media, etc.). In particular, forum debates on news websites are a very representative case of interaction between people using the Web 2.0.In this kind of interaction, as in real life, people play a social role [1]. Goffman [2] defines the social role as "the enactment of rights and duties attached to a given status". In other words, people have some regularities in their behaviour and this behaviour can be analysed in order to figure out the social role. Golder and Donath [3] study the Usenet newsgroup and they define the celebrity social role in order to represent people who are recognised in and by their community.As a result, in this paper we provide three major contributions: the theoretical formalisation of the 'celebrity' social role inspired by and based on previous anthropological studies; the experiments of this theoretical framework on data extracted from a news website using three different meta-criteria and a baseline-criterion; and a discussion about the social role extraction, and the relation with the kind of data we deal with.The paper continues as follows. At first, we discuss the related work concerning the topic of social role recognition in online media. Then, we present the theoretical framework based on the study of Golder and Donath [3] extended with three meta-criteria to take into account the special features of the forum debates on news websites. We continue by presenting the experimental framework and the dataset we used, and we discuss about the evaluation of the different criteria applied. We conclude by commenting on the results and discussing the outcome.

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Section: Discussion and Perspectives A) A Celebrity Is Not Just A mentioning

confidence: 99%

Extracting Celebrities from Online Discussions

Forestier¹,

Velcin²,

Stavrianou

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

View full text Add to dashboard Cite

show abstract

“…Pal et al [13] detect sentiments about a set of predefined themes in blogs. Stavrianou et al [14] propose an opinion-oriented graphical model for extracting information about opinions expressed in online forums. They focus on certain types of opinion information (such as opinion flow and attitudes of users), which cannot be extracted in the social network graphical model.…”

Section: Related Workmentioning

confidence: 99%

Co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community

Biyani

Caragea

Mitra

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Sentiment analysis has been widely researched in the domain of online review sites with the aim of getting summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users in online health communities such as cancer support forums, etc. Online health communities act as a medium through which people share their health concerns with fellow members of the community and get social support. Identifying sentiments expressed by members in a health community can be helpful in understanding dynamics of the community such as dominant health issues, emotional impacts of interactions on members, etc. In this work, we perform sentiment classification of user posts in an online cancer support community (Cancer Survivors Network). We use Domain-dependent and Domain-independent sentiment features as the two complementary views of a post and use them for post classification in a semi-supervised setting using the co-training algorithm. Experimental results demonstrate effectiveness of our methods.

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“…The number of replies a comment c ∈ V i receives is thus n(c) = card{c ∈ V i |(c , c) ∈ ρ * } We define a reply chain from c to c, similar to the definition employed in [16], as ν : C × {C ∪ S} → P{C},…”

Section: A Comment Level Structuresmentioning

confidence: 99%

Direct Negative Opinions in Online Discussions

Musat

Faltings

Rousille

2013

2013 International Conference on Social Computing

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Abstract-In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions -direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mining techniques.We create a model of human behavior in online communities, based on the network topology and on the communication content. The model contains seven hypotheses, which validate two intuitions. The first intuition is that the content of the messages exchanged in an online community can separate good and insightful contributions from the rest. The second intuition is that there is a delay until the network stabilizes and until standard measures, such as betweenness centrality, can be used accurately. Taken together, these intuitions are a solid case for using the content of the communication along with network measures. We show that the sentiment within the messages, especially antagonism, can significantly alter the community perception. We use real world data, taken from the Slashdot 1 discussion forum to validate our model. All the findings are accompanied by extremely significant t-test p-values.

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Definition and Measures of an Opinion Model for Mining Forums

Cited by 7 publications

References 10 publications

Extracting Celebrities from Online Discussions

Extracting Celebrities from Online Discussions

Co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community

Direct Negative Opinions in Online Discussions

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