ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Information extraction
- Creators: Davulcu, Hasan
Description
Analysis of political texts, which contains a huge amount of personal political opinions, sentiments, and emotions towards powerful individuals, leaders, organizations, and a large number of people, is an interesting task, which can lead to discover interesting interactions between the political parties and people. Recently, political blogosphere plays an increasingly important role in politics, as a forum for debating political issues. Most of the political weblogs are biased towards their political parties, and they generally express their sentiments towards their issues (i.e. leaders, topics etc.,) and also towards issues of the opposing parties. In this thesis, I have modeled the above interactions/debate as a sentimental bi-partite graph, a bi-partite graph with Blogs forming vertices of a disjoint set, and the issues (i.e. leaders, topics etc.,) forming the other disjoint set,and the edges between the two sets representing the sentiment of the blogs towards the issues. I have used American Political blog data to model the sentimental bi- partite graph, in particular, a set of popular political liberal and conservative blogs that have clearly declared positions. These blogs contain discussion about social, political, economic issues and related key individuals in their conservative/liberal view. To be more focused and more polarized, 22 most popular liberal/conservative blogs of a particular time period, May 2008 - October 2008(because of high intensity of debate and discussions), just before the presidential elections, was considered, involving around 23,800 articles. This thesis involves solving the questions: a) which is the most liberal/conservative blogs on the web? b) Who is on which side of debate and what are the issues? c) Who are the important leaders? d) How do you model the relationship between the participants of the debate and the underlying issues?
ContributorsThirumalai, Dananjayan (Author) / Davulcu, Hasan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
A story is defined as "an actor(s) taking action(s) that culminates in a resolution(s)''. I present novel sets of features to facilitate story detection among text via supervised classification and further reveal different forms within stories via unsupervised clustering. First, I investigate the utility of a new set of semantic features compared to standard keyword features combined with statistical features, such as density of part-of-speech (POS) tags and named entities, to develop a story classifier. The proposed semantic features are based on triplets that can be extracted using a shallow parser. Experimental results show that a model of memory-based semantic linguistic features alongside statistical features achieves better accuracy. Next, I further improve the performance of story detection with a novel algorithm which aggregates the triplets producing generalized concepts and relations. A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. The algorithm clusters triplets into generalized concepts by utilizing syntactic criteria based on common contexts and semantic corpus-based statistical criteria based on "contextual synonyms''. Generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant (36%) boost in performance for the story detection task. Finally, I implement co-clustering based on generalized concepts/relations to automatically detect story forms. Overlapping generalized concepts and relationships correspond to archetypes/targets and actions that characterize story forms. I perform co-clustering of stories using standard unigrams/bigrams and generalized concepts. I show that the residual error of factorization with concept-based features is significantly lower than the error with standard keyword-based features. I also present qualitative evaluations by a subject matter expert, which suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using standard keyword-based features.
ContributorsCeran, Saadet Betul (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven R. (Committee member) / Shakarian, Paulo (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2016