Journal Publications

  1. G. Paltoglou. Sentiment-based event detection in Twitter. Journal of the American Society for Information Science and Technology, in press. [Abstract]
    The main focus of the paper is to examine whether sentiment analysis can be successfully used for event detection, that is, detecting significant events that occur in the world. Most solutions to this problem are typically based on increases or spikes in frequency of terms in social media. In our case, we explore whether sudden changes in the positivity or negativity that keywords are typically associated with can be exploited for this purpose. A dataset that contains several million Twitter messages over a one-month time span is presented and experimental results demonstrate that sentiment analysis can be successfully utilized for this purpose. Further experiments study the sensitivity of both frequency or sentiment based solutions to a number of parameters. Concretely, we show that the number of tweets that are used for event detection is an important factor, while the number of days used to extract token frequency or sentiment averages isn’t. Lastly, we present results focusing on detecting local events and conclude that all approaches are dependant on the level of coverage that such events receive in social media.
  2. M. Salampasis, G. Paltoglou, A. Giachanou. Using social media for continuous monitoring and mining of consumer behaviour. International Journal of Electronic Business, 11(1): 85-96, 2014. [Abstract]
    Social communication and microblogging services have known unprecedented popularity in recent years. This new digital landscape, combined with the ubiquitous online access potential of modern devices, provides novel capabilities to online users and allows them to express their opinions and attitudes about everything in almost real time. In this paper, we present an investigation on the use of social media for the continuous monitoring and mining of consumer behaviour. We analyse hundreds of thousands of microblogging messages containing comments, sentiments and opinions about food and brand products. We present initial results, which confirm previous studies about the potential of using social media monitoring for branding purposes. The results provide strong indications that given the use of such services by millions of users, they can play a key role in supporting and enhancing important business processes. Examples of such processes include company-to-customer relationship management, brand image building and word-of-mouth (WoM) branding.
  3. P. Sobkowicz, M. Thelwall, K. Buckley, G. Paltoglou, A. Sobkowicz. Lognormal distributions of user post lengths in Internet discussions - a consequence of the Weber-Fechner law?. EPJ Data Science, 2:2, doi:10.1140/epjds14. [Abstract]
    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.
  4. J. Sienkiewicz, M. Skowron, G. Paltoglou, J. Holyst. Entropy-growth-based model of emotionally charged online dialogues. Advances in Complex Systems, 16(4n5): 1350026, 2013 (Impact factor: 0.647 ). [Abstract]
    We analyze emotionally annotated massive data from IRC (Internet Relay Chat) and model the dialogues between its participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution. This process is claimed to be correlated to the emergence of the power-law distribution of the discussion lengths observed in the dialogues. We perform numerical simulations based on the noticed phenomenon obtaining a good agreement with the real data. Finally, we propose a method to artificially prolong the duration of the discussion that relies on the entropy of emotional probability distribution.
  5. G. Paltoglou, M. Thelwall. Seeing stars of valence and arousal in blog posts. Journal of IEEE Transactions of Affective Computing, 99 (PrePrints):1, 2012 (est. Impact Factor: 2.500). [Abstract]
    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 Russell's 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.
  6. G. Paltoglou, M. Theunis, A. Kappas, M. Thelwall. Predicting emotional responses to long informal text. Journal of IEEE Transactions of Affective Computing, 99 (PrePrints):1, 2012 (est. Impact Factor: 2.500). [Abstract]
    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.
  7. A. Chmiel, J. Sienkiewicz, M. Thelwall M., G. Paltoglou, K. Buckley, A. Kappas A., J.A. Holyst. Collective Emotions Online and Their Influence on Community Life. PLoS ONE, 6(7): e22207, 2011 (Impact Factor: 4.351).
  8. M. Thelwall, K. Buckley, G. Paltoglou. Sentiment Strength Detection for the Social Web. Journal of the American Society for Information Science and Technology, 63(1):163-173, 2012. (Impact Factor: 2.300) (preprint). [Abstract]
    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, Runners World, BBC Forums) 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.
  9. G. Paltoglou, M. Thelwall. Twitter, MySpace, Digg: Unsupervised sentiment analysis in social media. ACM Transactions on Intelligent Systems and Technology, Special Issue on Search and Mining User Generated Contents, 3(4):66:1-66:19, 2012 (Special Issue, acceptance Rate: 14%). [Abstract]
    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.
  10. A. Chmiel, P. Sobkowicz, J. Sienkiewicz, G. Paltoglou, K. Buckley, M. Thelwall , J.A. Hołyst. Negative emotions boost user activity at BBC forum. Physica A: Statistical Mechanics and its Applications, 390 (16): 2936-2944, 2011 (Impact Factor: 1.562). [Abstract]
    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.
  11. M. Mitrovic, G. Paltoglou, B. Tadi. Quantitative analysis of bloggers collective behavior powered by emotions. Journal of Statistical Mechanics: Theory and Experiment, P02005, 2011 (Impact Factor: 2.670). [Abstract]
    Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study emergence of the emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite network of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion-classifier developed for this type of texts. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore robustness of these critical states, we design a network automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states.
  12. M. Thelwall, K. Buckley, G. Paltoglou. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2): 406-418, 2011 (Impact Factor: 2.300). [Abstract]
    The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions.
  13. M. Mitrovic, G. Paltoglou, B. Tadi. Networks and Emotion-Driven User Communities at Popular Blogs. The European Physical Journal B, 77 (4): 597-609, 2011 (Impact Factor: 1.466). [Abstract]
    Online communications at web portals represents technology-mediated user interactions, leading to massive data and potentially new techno-social phenomena not seen in real social mixing. Apart from being dynamically driven, the user interactions via posts is indirect, suggesting the importance of the contents of the posted material. We present a systematic way to study Blog data by combined approaches of physics of complex networks and computer science methods of text analysis. We are mapping the Blog data onto a bipartite network where users and posts with comments are two natural partitions. With the machine learning methods we classify the texts of posts and comments for their emotional contents as positive or negative, or otherwise objective (neutral). Using the spectral methods of weighted bipartite graphs, we identify topological communities featuring the users clustered around certain popular posts, and underly the role of emotional contents in the emergence and evolution of these communities.
  14. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, A. Kappas. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12): 2544-2558, 2010 (Impact Factor: 2.300). [Abstract]
    Online communications at web portals represents technology-mediated user interactions, leading to massive data and potentially new techno-social phenomena not seen in real social mixing. Apart from being dynamically driven, the user interactions via posts is indirect, suggesting the importance of the contents of the posted material. We present a systematic way to study Blog data by combined approaches of physics of complex networks and computer science methods of text analysis. We are mapping the Blog data onto a bipartite network where users and posts with comments are two natural partitions. With the machine learning methods we classify the texts of posts and comments for their emotional contents as positive or negative, or otherwise objective (neutral). Using the spectral methods of weighted bipartite graphs, we identify topological communities featuring the users clustered around certain popular posts, and underly the role of emotional contents in the emergence and evolution of these communities.
  15. S. Gobron, J. Ahn, G. Paltoglou, M. Thelwall, D. Thalmann. From Sentence to Emotion: Real-time Three-Dimensional Graphics Metaphor of Emotions Extracted from Text. The Visual Computer Journal, 26(8): 505-519,2010 (Impact Factor: 1.06). [Abstract]
    This paper presents a novel concept: a graphical representation of human emotion extracted from text sentences. The major contributions of this paper are the following. First, we present a pipeline that extracts, processes, and renders emotion of 3D virtual human (VH). The extraction of emotion is based on data mining statistic of large cyberspace databases. Second, we propose methods to optimize this computational pipeline so that real-time virtual reality rendering can be achieved on common PCs. Third, we use the Poisson distribution to transfer database extracted lexical and language parameters into coherent intensities of valence and arousal—parameters of Russell’s circumplex model of emotion. The last contribution is a practical color interpretation of emotion that influences the emotional aspect of rendered VHs. To test our method’s efficiency, computational statistics related to classical or untypical cases of emotion are provided. In order to evaluate our approach, we applied our method to diverse areas such as cyberspace forums, comics, and theater dialogs.
  16. G. Paltoglou, M. Salampasis, M. Satratzemi. Collection-integral Source Selection for uncooperative distributed information retrieval environments. Information Sciences, 180(14): 2763-2776, 2010 (Impact Factor: 3.095). [Abstract]
    We propose a new integral-based source selection algorithm for uncooperative distributed information retrieval environments. The algorithm functions by modeling each source as a plot, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index. Based on the above modeling, the algorithm locates the collections that contain the most relevant documents. A number of transformations are applied to the original plot, in order to reward collections that have higher scoring documents and dampen the effect of collections returning an excessive number of documents. The family of linear interpolant functions that pass through the points of the modified plot is computed for each available source and the area that they cover in the rank-relevance space is calculated. Information sources are ranked based on the area that they cover. Based on this novel metric for collection relevance, the algorithm is tested in a variety of testbeds in both recall and precision oriented settings and its performance is found to be better or at least equal to previous state-of-the-art approaches, overall constituting a very effective and robust solution.
  17. G. Paltoglou, M. Salampasis, M. Satratzemi. Modeling information sources as integrals for effective and efficient source selection. Information Processing and Management Journal, 47(1): 18-36, 2011 (Impact Factor: 1.852). [Abstract]
    In this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm functions by modeling each information source as an integral, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index and selects the collections that cover the largest area in the rank-relevance space. Based on the above novel metric, the algorithm explicitly focuses on addressing the two goals of source selection; high-recall, which is important for source recommendation applications and high-precision which is important for distributed information retrieval, aiming to produce a high-precision final merged list. For the latter goal in particular, the new approach steps away from the usual practice of DIR systems of explicitly declaring the number of collections that must be queried and instead focuses solely on the number of retrieved documents in the final merged list, dynamically calculating the number of collections that are selected and the number of documents requested from each. The algorithm is tested in a wide range of testbeds in both recall and precision-oriented settings and its effectiveness is found to be equal or better than other state-of-the-art algorithms.
  18. G. Paltoglou, M. Salampasis, M. Satratzemi. A results merging algorithm for distributed information retrieval environments that combines regression methodologies with a selective download phase, . Information Processing and Management, 44(4): 1580-1599, 2008 (Impact Factor: 1.546). [Abstract]
    The problem of results merging in distributed information retrieval environments has gained significant attention the last years. Two generic approaches have been introduced in research. The first approach aims at estimating the relevance of the documents returned from the remote collections through ad hoc methodologies (such as weighted score merging, regression etc.) while the other is based on downloading all the documents locally, completely or partially, in order to calculate their relevance. Both approaches have advantages and disadvantages. Download methodologies are more effective but they pose a significant overhead on the process in terms of time and bandwidth. Approaches that rely solely on estimation on the other hand, usually depend on document relevance scores being reported by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging algorithms, need a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced is based on adaptively downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. Thus it reconciles the above two approaches, combining their strengths, while minimizing their drawbacks, achieving the limited time and bandwidth overhead of the estimation approaches and the increased effectiveness of the download. The proposed algorithm is tested in a variety of settings and its performance is found to be significantly better than the former, while approximating that of the latter.

Book Chapters

  1. G. Paltoglou. Sentiment Analysis in Social Media, In: Agarwal N. and Lim, M. and Wigard R.T. Online Collective Action: Dynamics of the Crowd in Social Media, Lecture Notes in Social Networks Series, Springer International Publishing, pages 3-17, 2013. [Abstract]
    Sentiment Analysis deals with the detection and analysis of affective content in written text. It utilizes methodologies, theories and techniques from a diverse set of scientific domains, ranging from psychology and sociology to natural language processing and machine learning. In this chapter we discuss the contributions of the field in social media analysis with a particular focus in online collective actions; as these actions are typically motivated and driven by intense emotional states (e.g., anger), sentiment analysis can provide unique insights into the inner workings of such phenomena throughout their life cycle. We also present the state-of-the-art in the field and describe some of its contributions into understanding online collective behavior. Lastly, we discuss significant real-world datasets that have been successfully utilized in research and are available for scientific purposes and also present a diverse set of available tools for conducting sentiment analysis.
  2. A. Chmiel, J. Sienkiewicz, G. Paltoglou, K. Buckley, M. Skowron, M. Thelwall, A. Kappas, J. A. Hołyst. Collective Emotions Online. In: N. Agarwal and M. Lim and R.T.Wigard, Online Collective Action: Dynamics of the Crowd in Social Media, Lecture Notes in Social Networks Series, Springer International Publishing, pages 59-74, 2014.
  3. S. Gonzalez-Bailon, G. Paltoglou. Signals of public opinion in online communication: A comparison of methods and data sources. In: D. V. Shah, J. Capella and W. R. Neuman, Toward Computational Social Science: Big Data in Digital Environments, ANNALS of the American Academy of Political and Social Science, 2014 (in press).
  4. G. Paltoglou, A. Giahannou. Opinion Retrieval: Searching for Opinions in social media. In: G. Paltoglou, F. Loizides, P. Hansen, Professional Search in the Modern World, Lecture Notes in Computer Science, Springer International Publishing, 2014 (in press).

Books/ Edited Books

  1. G. Paltoglou, F. Loizides, P. Hansen. Professional Search in the Modern World, Lecture Notes in Computer Science (LNCS 8830), Springer International Publishing, 2014 (in press).
  2. M. Santini, M. Oakes, G. Paltoglou. Computational Theory of Digital Genre: Genre Theory in the Context of Text Analytics, Springer International Publishing, 2015 (in preparation).

Conference/Workshop Publications

  1. G. Paltoglou. Using Twitter and Sentiment Analysis for event detection. In LREC 2014: 9th International Conference on Language Resources and Evaluation, 2014.
  2. G. Paltoglou, M. Thelwall. More than bag-of-words: Sentence-based document representation for sentiment analysis . In RANLP 2013: Recent Advances in Natural Language Processing, pages 546-552, 2013. [Abstract]
    Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document representation as their basis. In this paper, we examine whether a more fine-grained representation of documents as sequences of emotionally-annotated sentences can increase document classification accuracy. Experiments conducted on a sentence and document level annotated corpus show that the proposed solution, combined with BoW features, offers an increase in classification accuracy.
  3. G. Paltoglou, K. Buckley. Subjectivity annotation of the Microblog 2011 Realtime Adhoc relevance judgments. In ECIR 2013: 35th European Conference on Information Retrieval, pages 344 - 355, 2013 (Acceptance Rate: 16%). [Abstract]
    In this work, we extend the Microblog dataset with subjectivity annotations. Our aim is twofold; first, we want to provide a high-quality, multiply-annotated gold standard of subjectivity annotations for the relevance assessments of the real-time adhoc task. Second, we randomly sample the rest of the dataset and annotate it for subjectivity once, in order to create a complementary annotated dataset that is at least an order of magnitude larger than the gold standard. As a result we have 2,389 tweets that have been annotated by multiple humans and 75,761 tweets that have been annotated by one annotator. We discuss issues like inter-annotator agreement, the time that it took annotators to classify tweets in correlation to their subjective content and lastly, the distribution of subjective tweets in relation to topic categorization. The annotated datasets and all relevant anonymised information are freely available for research purposes.
  4. M. Salampasis, G. Paltoglou, A. Giahanou. Report on the CLEF-IP 2012 Experiments: Search of Topically Organized Patents. In CLEF 2012: Conference and Labs of the Evaluation Forum, Online Working Notes/Labs/Workshop, 2012. [Abstract]
    This technical report presents the work which has been carried out using Distributed Information Retrieval methods for federated search of patent documents for the passage retrieval starting from claims (patentability or novelty search) task. Patent documents produced worldwide have manually assigned classification codes which in our work are used to cluster, distribute and index patents through hundreds or thousands of sub-collections. We tested different combinations of source selection (CORI, BordaFuse, Reciprocal Rank) and results merging algorithms (SSL, CORI). We also tested different combinations of the number of collections requested and documents retrieved from each collection. One of the aims of the experiments was to test older DIR methods that characterize different collections using collection statistics like term frequencies and how they perform in patent search. Also to experiment with newer DIR methods which focus on explicitly estimating the number of relevant documents in each collection and usually attain improvements in precision over previous approaches, but their recall is usually lower. However, the most important aim was to examine how DIR methods will perform if patents are topically organized using their IPC and if DIR methods can approximate the performance of a centralized index approach. We submitted 8 runs. According to PRES @100 our best DIR approach ranked 7th across 31 submitted results, however our best DIR (not submitted) run outperforms all submitted runs.
  5. G. Paltoglou, M. Thelwall. University of Wolverhampton at the TREC-2012 Microblog Track. In TREC 2012: 20th Text REtrieval Conference, Gaithersburg, Maryland, 2011.
  6. M. Salampasis, G. Paltoglou, A. Giahanou. Using Social Media for Continuous Monitoring and Mining of Consumer Behaviour. In HAICTA 2011: 5th International Conference on Information and Communication Technologies in Agriculture, 2011.
  7. St. Gobron , J. Ahn, Q. Silvestre, D. Thalmann, S. Rank, M. Skowron, G. Paltoglou, M. Thelwall. An Interdisciplinary VR-architecture for 3D chatting with non-verbal communication. In EGVE 2011: Joint Virtual Reality Conference of EuroVR, pages 87 - 94, 2011.
  8. M. Skowron, H. Pirker, S. Rank, G. Paltoglou, J. Ahn, S. Gobron. No peanuts! Affective Cues for the Virtual Bartender. In 24th Florida Artificial Intelligence Research Society Conference, 2011.
  9. M. Skowron, G. Paltoglou. Affect Bartender - Affective Cues and Their Application in a Conversational Agent. In 2011 Workshop on Affective Computational Intelligence, pages 1 - 7, 2011.
  10. G. Paltoglou, S. Gobron, M. Skowron, M. Thelwall, D. Thalmann. Sentiment analysis of informal textual communication in cyberspace. In Engage 2010, Springer LNCS State-of-the-Art Survey, pages 13 - 25, 2010.
  11. G. Paltoglou, M. Thelwall. A study of Information Retrieval weighting schemes for sentiment analysis. In ACL 2010: 48th Annual Meeting of the Association for Computational Linguistics, pages 1386 - 1395, 2010 (Acceptance Rate: 25%).
  12. G. Paltoglou, M. Thelwall, K. Buckley. Online textual communications annotated with grades of emotion strength, In 3rd International Workshop of Emotion: Corpora for research on Emotion and Affect, pages 25 - 31, 2010.
  13. G. Paltoglou, M. Salampasis, M. Satratzemi. Simple adaptations of data fusion algorithms for source selection. In ECIR 2009: 31st European Conference on Information Retrieval, pages 497 - 508, 2009 (Acceptance Rate: 22%).
  14. G. Paltoglou, M. Salampasis, M. Satratzemi. Integral Based Source Selection for Uncooperative Distributed Information Retrieval Environments. In LSDS-IR 2008: Large-Scale Distributed Systems for Information Retrieval, pages 67 - 74, 2008.
  15. G. Paltoglou, M. Salampasis, F. Lazarinis. Indexing and Retrieval of a Greek Corpus In iNEWS 2008: Improving Non English Web Searching, pages 47 - 54, 2008.
  16. G. Paltoglou, M. Salampasis, M. Satratzemi. A Comparison of Centralized and Distributed Information Retrieval approaches. In PCI 2008: 12th Panhellenic Conference on Informatics, pages 21 - 25, 2008.
  17. G. Paltoglou, M. Salampasis, M. Satratzemi. Hybrid Results Merging. In CIKM 2007: 16th Conference on Information and Knowledge Management, pages 321 - 330, 2007 (Acceptance Rate: 17%).
  18. G. Paltoglou, M. Salampasis, M. Satratzemi. Results Merging Algorithm Using Multiple Regression Models. In ECIR 2007: 29th European Conference on Information Retrieval, pages 173 - 184, 2007 (Acceptance Rate: 19%).
  19. G. Paltoglou, M. Salampasis, M. Satratzemi, G. Evangelidis. Using linkage information to approximate the distribution of relevant documents in DIR In PCI 2007: 11th Panhellenic Conference on Informatics, pages 234 - 244, 2007.