Georgios Paltoglou
University of Wolverhampton
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Publication
Featured researches published by Georgios Paltoglou.
Journal of the Association for Information Science and Technology | 2012
Mike Thelwall; Kevan Buckley; Georgios Paltoglou
Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
Journal of the Association for Information Science and Technology | 2010
Mike Thelwall; Kevan Buckley; Georgios Paltoglou; Di Cai; Arvid Kappas
A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6p accuracy and negative emotion with 72.8p accuracy, both based upon strength scales of 1–5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.
Physica A-statistical Mechanics and Its Applications | 2011
Anna Chmiel; Pawel Sobkowicz; Julian Sienkiewicz; Georgios Paltoglou; Kevan Buckley; Mike Thelwall; Janusz A. Hołyst
We present an empirical study of user activity in online BBC discussion forums, measured by the number of posts written by individual debaters and the average sentiment of these posts. Nearly 2.5 million posts from over 18 thousand users were investigated. Scale-free distributions were observed for activity in individual discussion threads as well as for overall activity. The number of unique users in a thread normalized by the thread length decays with thread length, suggesting that thread life is sustained by mutual discussions rather than by independent comments. Automatic sentiment analysis shows that most posts contain negative emotions and the most active users in individual threads express predominantly negative sentiments. It follows that the average emotion of longer threads is more negative and that threads can be sustained by negative comments. An agent-based computer simulation model has been used to reproduce several essential characteristics of the analyzed system. The model stresses the role of discussions between users, especially emotionally laden quarrels between supporters of opposite opinions, and represents many observed statistics of the forum.
ACM Transactions on Intelligent Systems and Technology | 2012
Georgios Paltoglou; Mike Thelwall
Sentiment analysis is a growing area of research with significant applications in both industry and academia. Most of the proposed solutions are centered around supervised, machine learning approaches and review-oriented datasets. In this article, we focus on the more common informal textual communication on the Web, such as online discussions, tweets and social network comments and propose an intuitive, less domain-specific, unsupervised, lexicon-based approach that estimates the level of emotional intensity contained in text in order to make a prediction. Our approach can be applied to, and is tested in, two different but complementary contexts: subjectivity detection and polarity classification. Extensive experiments were carried on three real-world datasets, extracted from online social Web sites and annotated by human evaluators, against state-of-the-art supervised approaches. The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web.
IEEE Transactions on Affective Computing | 2013
Georgios Paltoglou; Mathias Theunis; Arvid Kappas; Mike Thelwall
Most sentiment analysis approaches deal with binary or ordinal prediction of affective states (e.g., positive versus negative) on review-related content from the perspective of the author. The present work focuses on predicting the emotional responses of online communication in nonreview social media on a real-valued scale on the two affective dimensions of valence and arousal. For this, a new dataset is introduced, together with a detailed description of the process that was followed to create it. Important phenomena such as correlations between different affective dimensions and intercoder agreement are thoroughly discussed and analyzed. Various methodologies for automatically predicting those states are also presented and evaluated. The results show that the prediction of intricate emotional states is possible, obtaining at best a correlation of 0.89 for valence and 0.42 for arousal with the human assigned assessments.
IEEE Transactions on Affective Computing | 2013
Georgios Paltoglou; Mike Thelwall
Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russells circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.
international conference on computational linguistics | 2013
Mike Thelwall; Kevan Buckley; Georgios Paltoglou; Marcin Skowron; David Garcia; Stéphane Gobron; Junghyun Ahn; Arvid Kappas; Dennis Küster; Janusz A. Hołyst
Sentiment analysis programs are now sometimes used to detect patterns of sentiment use over time in online communication and to help automated systems interact better with users. Nevertheless, it seems that no previous published study has assessed whether the position of individual texts within on-going communication can be exploited to help detect their sentiments. This article assesses apparent sentiment anomalies in on-going communication --- texts assigned significantly different sentiment strength to the average of previous texts --- to see whether their classification can be improved. The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.
Annals of The American Academy of Political and Social Science | 2015
Sandra González-Bailón; Georgios Paltoglou
This study offers a systematic comparison of automated content analysis tools. The ability of different lexicons to correctly identify affective tone (e.g., positive vs. negative) is assessed in different social media environments. Our comparisons examine the reliability and validity of publicly available, off-the-shelf classifiers. We use datasets from a range of online sources that vary in the diversity and formality of the language used, and we apply different classifiers to extract information about the affective tone in these datasets. We first measure agreement (reliability test) and then compare their classifications with the benchmark of human coding (validity test). Our analyses show that validity and reliability vary with the formality and diversity of the text; we also show that ready-to-use methods leave much space for improvement when analyzing domain-specific content and that a machine-learning approach offers more accurate predictions across communication domains.
EPJ Data Science | 2013
Pawel Sobkowicz; Mike Thelwall; Kevan Buckley; Georgios Paltoglou; Antoni Sobkowicz
The paper presents an analysis of the length of comments posted in Internet discussion fora, based on a collection of large datasets from several sources. We found that despite differences in the forum language, the discussed topics and user emotions, the comment length distributions are very regular and described by the lognormal form with a very high precision. We discuss possible origins of this regularity and the existence of a universal mechanism deciding the length of the user posts. We suggest that the observed lognormal dependence may be due to an entropy maximizing combination of two psychological factors which are perceived on a non-linear, logarithmic scale in accordance with the Weber-Fechner law, namely the time spent on post related considerations and the comment length itself. This hypothesis is supported by an experimental check of text length recognition capacity, confirming proportionality of the ‘just noticeable differences’ for text lengths - the basis of the Weber-Fechner law.
eurographics | 2011
Stéphane Gobron; Junghyun Ahn; Quentin Silvestre; Daniel Thalmann; Stefan Rank; Marcin Skowron; Georgios Paltoglou; Mike Thelwall
The communication between avatar and agent has already been treated from different but specialized perspectives. In contrast, this paper gives a balanced view of every key architectural aspect: from text analysis to computer graphics, the chatting system and the emotional model. Non-verbal communication, such as facial expression, gaze, or head orientation is crucial to simulate realistic behavior, but is still an aspect neglected in the simulation of virtual societies. In response, this paper aims to present the necessary modularity to allow virtual humans (VH) conversation with consistent facial expression -either between two users through their avatars, between an avatar and an agent, or even between an avatar and a Wizard of Oz. We believe such an approach is particularly suitable for the design and implementation of applications involving VHs interaction in virtual worlds. To this end, three key features are needed to design and implement this system entitled 3D-emoChatting. First, a global architecture that combines components from several research fields. Second, a real-time analysis and management of emotions that allows interactive dialogues with non-verbal communication. Third, a model of a virtual emotional mind called emoMind that allows to simulate individual emotional characteristics. To conclude the paper, we briefly present the basic description of a user-test which is beyond the scope of the present paper.