Georgios Paltoglou

• University of Wolverhampton

Sentiment analysis deals with the computational detection and extraction of opinions, beliefs and emotions in written text. It combines theories and methodologies from a diverse set of scientific domains, such as psychology, natural language processing and machine learning. It fulfils the very important role of transforming the unstructured textual...

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 pap...

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...

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, i...

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.

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, twe...

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 len...

We performed statistical analysis on data from the Digg.com website, which enables its users to express their opinion on news stories by taking part in forum-like discussions as well as directly evaluate previous posts and stories by assigning so called "diggs". Owing to fact that the content of each post has been annotated with its emotional value...

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 algorit...

Emotions are an important part of most societal dynamics. As with face to face meetings, Internet exchanges may not only include factual information but may also elicit emotional responses; how participants feel about the subject discussed or other group members. The development of automatic sentiment analysis has made large scale emotion detection...

We perform a statistical analysis of emotionally annotated comments in two large online datasets, examining chains of consecutive posts in the discussions. Using comparisons with randomised data we show that there is a high level of correlation for the emotional content of messages.

E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information--how participants feel about the subject discussed or other group members. Emotions in turn are known to be important...

E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information - how participants feel about the subject discussed or other group members. Emotions are known to be important in aff...

This paper presents methods for the detection of textual expressions of users' affective states and explores an application of these affective cues in a conversational system - Affect Bartender. We also describe the architecture of the system, core system components and a range of developed communication interfaces. The application of the described...

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...

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 col...

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 ori...

The aim of this paper is threefold: it explores methods for the detection of affective states in text, it presents the usage of such affective cues in a conversational sys- tem and it evaluates its effectiveness in a virtual reality setting. Valence and arousal values, used for generating facial expressions of users' avatars, are also incorpo- rate...

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 emot...

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 w...

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...

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 sy...

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 algo...

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...

Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis pro...

The ability to correctly identify the existence and polarity of emotion in informal, textual communication is a very important part of a realistic and immersive 3D environment where people communicate with one another through avatars or with an automated system. Such a feature would provide the system the ability to realistically represent the mood...

Source selection deals with the problem of selecting the most appropriate information sources from the set of, usually non-intersecting, available document collections. On the other hand, data fusion techniques (also known as metasearch techniques) deal with the problem of aggregating the results from multiple, usually completely or partly intersec...

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 col...

Greek is one of the most difficult languages to handle in Web Information Retrieval (IR) related tasks. Its difficulty stems from the fact that it is grammatically, morphologically and orthographically more complex than the lingua franca of IR, English. In this paper, we address a significant number of issues that originate from the Greek language....

Distributed Information Retrieval (DIR) has been suggested to offer a prospective solution to a number of issues concerning information retrieval in the WWW. On the other hand, previous studies have indicated that centralized approaches offer the best solution for optimal quality of result (i.e. effectiveness). In this paper, we revisit those claim...

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 merg...

The problem of results merging in distributed information retrieval environments has been approached by two different directions in research. Estimation approaches attempt to calculate the relevance of the returned documents through ad-hoc methodologies (weighted score merging, regression etc) while download approaches, download all the documents l...

This paper describes a new algorithm for merging the results of remote collections in a distributed information retrieval environment. The algorithm makes use only of the ranks of the returned documents, thus making it very efficient in environments where the remote collections provide the minimum of cooperation. Assuming that the correlation betwe...

2 vol.