The Uses of Objects Task is a widely used assessment of creative performance, but it relies on subjective scoring methods for evaluation. A new version of the task was devised using Latent Semantic Analysis (LSA), a computational tool used to measure semantic distance. 135 participants provided as many creative uses for as they could for 20 separate objects. Responses were analyzed for strategy use, category switching, variety, and originality of responses, as well as subjective measure of creativity by independent raters. The LSA originality measure was more reliable than the subjective measure, and values averaged over participants correlated with both subjective evaluations and self-assessment of creativity. The score appeared to successfully isolate the creativity of the people themselves, rather than the potential creativity afforded by a given object.

The Uses of Objects Task is a widely used assessment of creative performance, but it relies on subjective scoring methods for evaluation. A new version of the task was devised using Latent Semantic Analysis (LSA), a computational tool used to measure semantic distance. 135 participants provided as many creative uses for as they could for 20 separate objects. Responses were analyzed for strategy use, category switching, variety, and originality of responses, as well as subjective measure of creativity by independent raters. The LSA originality measure was more reliable than the subjective measure, and values averaged over participants correlated with both subjective evaluations and self-assessment of creativity. The score appeared to successfully isolate the creativity of the people themselves, rather than the potential creativity afforded by a given object.

In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents (customer comments) at two levels of abstraction for a quality assurance setting. Then two computer algorithms, LSA and LDA, were used to perform classification on these documents. The results indicate that humans outperformed all computer algorithms and had an accuracy rate of 94% at the higher level of abstraction and 76% at the lower level of abstraction. At the high level of abstraction, the accuracy rates were 84% for both LSA and LDA and at the lower level, the accuracy rate were 67% for LSA and 64% for LDA. The findings of this research have many strong implications for the improvement of information systems that process unstructured text. Document classifiers have many potential applications in many fields (e.g., fraud detection, information retrieval, national security, and customer management). Development and refinement of algorithms that classify text is a fruitful area of ongoing research and this dissertation contributes to this area.

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.

Conventional pattern recognition systems have two components: feature analysis and pattern classification. For any object in an image, features could be considered as the major characteristic of the object either for object recognition or object tracking purpose. Features extracted from a training image, can be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable scene analysis, it is important that the features extracted from the training image are detectable even under changes in image scale, noise and illumination. Scale invariant feature has wide applications such as image classification, object recognition and object tracking in the image processing area. In this thesis, color feature and SIFT (scale invariant feature transform) are considered to be scale invariant feature. The classification, recognition and tracking result were evaluated with novel evaluation criterion and compared with some existing methods. I also studied different types of scale invariant feature for the purpose of solving scene analysis problems. I propose probabilistic models as the foundation of analysis scene scenario of images. In order to differential the content of image, I develop novel algorithms for the adaptive combination for multiple features extracted from images. I demonstrate the performance of the developed algorithm on several scene analysis tasks, including object tracking, video stabilization, medical video segmentation and scene classification.

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.

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.

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.

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.