People share a big part of their lives and opinions on platforms such as Facebook and Twitter. The companies behind these sites do their absolute best to collect as much data as possible. This data could be used to extract opinions in many different ways. Every company, organization or public person is probably curious on what is being said about them right now. There are also areas where opinions are related to the outcome of an event. Examples of such events are presidential elections or the Eurovision Song Contest. In these events, peoples’ votes will directly reflect the outcome of the elections or contests. We have developed a simplistic prototype that is able to predict the result of the Eurovision Song Contest using sentiment analysis on tweets. The prototype collects tweets about the event, performs sentiment analysis, and uses different filters to predict the ranks of the contestants. We evaluted our results with the actual voting results of the event and found a Pearson correlation of approximately 0.65. With more time and resources we believe that it is possible to create a highly accurate prediction model. It could be used in lots of different contexts. Politicians and their parties could use it to evaluate their campaigns. The press could use it to create more interesting news reports. Companies would be able to investigate their brand appreciation. A system like this could be used in many different fields.

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.

With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence. / Ph. D.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / Gurdip Singh / In the recent years, social networks have become an integral part of our lives. Their outgrowth has resulted in opportunities for interesting data mining problems, such as interest or friendship recommendations. A global ontology over the interests specified by the users of a social network is essential for accurate recommendations. The focus of this work is on engineering such an interest ontology. In particular, given that the resulting ontology is meant to be used for data mining applications to social network problems, we explore only hierarchical ontologies. We propose, evaluate and compare three approaches to engineer an interest hierarchy. The proposed approaches make use of two popular knowledge bases, Wikipedia and Directory Mozilla, to extract interest definitions and/or relationships between interests. More precisely, the first approach uses Wikipedia to find interest definitions, the latent semantic analysis technique to measure the similarity between interests based on their definitions, and an agglomerative clustering algorithm to group similar interests into higher level concepts. The second approach uses the Wikipedia Category Graph to extract relationships between interests. Similarly, the third approach uses Directory Mozilla to extract relationships between interests. Our results indicate that the third approach, although the simplest, is the most effective for building an ontology over user interests. We use the ontology produced by the third approach to construct interest based features. These features are further used to learn classifiers for the friendship prediction task. The results show the usefulness of the ontology with respect to the results obtained in absence of the ontology.

This thesis offers an insight into the way of exploring dance in nō theater as the "language of gestures", which it subjects to semantic analysis. The author deals with the formal structure of dance in nō - its historical origins and formal changes related to the context, and brings an analysis of the nowadays form and its components. The thesis also focuses on the process of semantic reception of dance - it examines the relationship of actors and audience in the historical and socio-cultural context, as well as the changes in semantic reception. Above all, a detailed analysis and translation of basic structural and semantic units of movement vocabulary is presented and subsequently used in specific semiological analysis of the choreography kuse from the play Hagoromo. Keywords: Japanese dance, nō theater, semantic analysis, Hagoromo

Thesis (M. A. (African Languages)) -- University of Limpopo, 2002 / Refer to the document

Thesis (M.A. (African Languages)) -- University of Limpopo, 2002 / Refer to document

While a growing evidence base suggests that expressive writing about a traumatic event may be an effective intervention which results in a variety of health benefits, there are still multiple competing theories that seek to explain expressive writing’s mechanism(s) of action. Two of the theories with stronger evidence bases are exposure theory and cognitive processing theory. The state of this field is complicated by methodological limitations; operationalizing and measuring the relative constructs of trauma narratives, such as coherence, traditionally requires time- and labor-intensive methods such as using a narrative coding scheme. This study used a computer-based methodology, latent semantic analysis (LSA), to quantify narrative coherence and analyze the relationship between narrative coherence and both short- and long-term outcomes of expressive writing. A subsample of unscreened undergraduates (N=113) who had been randomly assigned to the expressive writing group of a larger study wrote about the most traumatic event that had happened to them for three twenty-minute sessions; their narratives were analyzed using LSA. There were three main hypotheses, informed by cognitive processing theory: 1) That higher coherence in a given session would be associated with a more positive reported valence at the conclusion of that session, 2) that increasing narrative coherence across writing sessions would be associated with increasing reported valence at the conclusion of each session, and 3) that increasing narrative coherence over time would be associated with a decrease in post-traumatic stress symptoms. Overall, initial hypotheses were not supported, but higher coherence in the third writing session was associated with more negative valence at the conclusion of the session. Furthermore, relationships between pre- and post-session valence strengthened over time, and coherence, pre-session valence, and post-session valence all trended over time. These results suggest a collection of temporal effects, the implications of which are discussed in terms of future directions.

The semantic content (i.e., meaning of words) is the essence of retrieved autobiographical memories. In comparison to previous research, which has mainly focused on phenomenological experiences and age distribution of memory events, the present study provides a novel view on the retrieval of event information by addressing the semantic representation of memories. In the present study the semantic representation (i.e., word locations represented by vectors in a high dimensional space) of retrieved memory information were investigated, by analyzing the data with an automatic statistical algorithm. The experiment comprised a cued recall task, where participants were presented with unimodal (i.e., one sense modality) or multimodal (i.e., three sense modalities in conjunction) retrieval cues and asked to recall autobiographical memories. The memories were verbally narrated, recorded and transcribed to text. The semantic content of the memory narrations was analyzed with a semantic representation generated by latent semantic analysis (LSA). The results indicated that the semantic representation of visually evoked memories were most similar to the multimodally evoked memories, followed by auditorily and olfactorily evoked memories. By categorizing the semantic content into clusters, the present study also identified unique characteristics in the memory content across modalities.