Statistical analysis of emotions and opinions at Digg website

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, apart from the strictly structural properties, the study also includes an analysis of the average emotional response of the posts commenting the main story. While analysing correlations at the story level, an interesting relationship between the number of diggs and the number of comments received by a story was found. The correlation between the two quantities is high for data where small threads dominate and consistently decreases for longer threads. However, while the correlation of the number of diggs and the average emotional response tends to grow for longer threads, correlations between numbers of comments and the average emotional response are almost zero. We also show that the initial set of comments given to a story has a substantial impact on the further "life" of the discussion: high negative average emotions in the first 10 comments lead to longer threads while the opposite situation results in shorter discussions. We also suggest presence of two different mechanisms governing the evolution of the discussion and, consequently, its length.Comment: 26 pages, 16 figures, 6 table

From sentence to emotion: a real-time three-dimensional graphics metaphor of emotions extracted from text

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 dialog

Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions

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

Emotional Analysis of Blogs and Forums Data

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.Comment: REVTEX format, 5 pages, 6 figures, 2 tables, accepted to Acta Physica Polonica

Presenting KAPODI – The Searchable Database of Emotional Stimuli Sets

Emotional stimuli such as images, words, or video clips are often used in studies researching emotion. New sets are continuously being published, creating an immense number of available sets and complicating the task for researchers who are looking for suitable stimuli. This paper presents the KAPODI-database of emotional stimuli sets that are freely available or available upon request. Over 45 aspects including over 25 key set characteristics have been extracted and listed for each set. The database facilitates finding of and comparison between individual sets. It currently contains sets published between 1963 and 2020. A searchable online version (https://airtable.com/shrnVoUZrwu6riP9b) allows users to select specific set characteristics and to find matching sets accordingly, as well as to add new published sets

Negative emotions boost users activity at BBC Forum

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.Comment: 29 pages, 6 figure

Collective emotions online and their influence on community life

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 affecting interaction partners in offline communication in many ways. Could emotions in Internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e-communities? The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. It is not clear if emotions in e-communities primarily derive from individual group members' personalities or if they result from intra-group interactions, and whether they influence group activities. We show the collective character of affective phenomena on a large scale as observed in 4 million posts downloaded from Blogs, Digg and BBC forums. To test whether the emotions of a community member may influence the emotions of others, posts were grouped into clusters of messages with similar emotional valences. The frequency of long clusters was much higher than it would be if emotions occurred at random. Distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length. For BBC forum threads, average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions. Our results prove that collective emotional states can be created and modulated via Internet communication and that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON

CYBEREMOTIONS – Collective Emotions in Cyberspace

AbstractEmotions 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 and analysis possible using text messages collected from the web. We present results of two years of studies performed in the EU Large Scale Integrating Project CYBEREMOTIONS (Collective emotions in cyberspace) Our goal is to understand the role of collective emotions in creating, forming and breaking-up ICT mediated communities and to prepare the background for the next generation of emotionally-intelligent ICT services. Project results have already attracted a lot of attention from various mass media and research journals including the Science and New Scientist magazines. Nine Project teams are organised in three layers (data, theory and ICT output)

Αλγόριθμοι και στρατηγικές επιλογής πηγών πληροφοριών και σύνθεσης αποτελεσμάτων (collection fusion algorithms) σε κατανεμημένα συστήματα αναζήτησης πληροφοριών

General purpose search engines, such as Google and Yahoo!, provide an easy mechanism for users to discover information on the Web. Despite their obvious advantages, they have a number of significant limitations, because they cannot reach or analyze a significant part of the information that is available. Distributed Information Retrieval systems, employing collection fusion algorithms, offer a solution to the above problem, by allowing users to submit queries to multiple information sources simultaneously through a single interface, offering a much wider coverage of the available information. This thesis deals with two of the main issues of designing and implementing efficient and effective Distributed Information Retrieval systems: source selection and results merging. The former deals with the ability of the system to select the most appropriate information sources to delegate the user query and the latter aims to produce the best possible final document list by merging to individual retrieved documents lists from the selected sources. The new algorithms that are presented in this thesis are designed to function effectively in settings where information sources provide no cooperation at all, thus making them applicable in the widest possible set of environments and domains. The source selection algorithm that is put forth provides a novel modeling of information sources as regions in a space created by the documents that they contain. It provides a full theoretical framework for addressing the source selection problem, while at the same time effectively captures real-world observations and widely accepted notions in Information Retrieval. Extensive experiments demonstrate that it is able to obtain a performance that is at least as good as other state-of-the-art approaches and more often better. The novel result merging algorithms that are presented are based on the supposition that search engines return only ranked lists of documents, without relevance scores, a scenario which is standard practice in current retrieval systems. They are both able to address the lack of information very effectively, demonstrating significant performance gains over other state-of-the-art approaches. Additionally, the second algorithm unites the two general directions that the results merging problem has been approached in research, combining their advantages while minimizing their drawbacks