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No / This paper describes a novel procedure for thinning binary text images by generating graphical representations of words within the image. A smoothed polygonal approximation of the boundaries of each word is first decomposed into a set of contiguous triangles. Each triangle is then classified into one of only three possible types from which a graph is generated that represents the topological features of the object. Joining graph points with straight lines generates a final polygon skeleton that, by construction, is one pixel wide and fully connected. Results of applying the procedure to thinning Arabic and English handwriting are presented. Comparisons of skeleton structure and execution time with results from alternative techniques are also presented. The procedure is considerably faster than the alternatives tested when the image resolution is greater than 600 dpi and the graphical representation often needed in subsequent recognition steps is available without further processing.

With the prevalence of large data stored in the cloud, including unstructured information in the form of text, there is now an increased emphasis on text mining. A broad range of techniques are now used for text mining, including algorithms adapted from machine learning, NLP, computational linguistics, and data mining. Applications are also multi-fold, including classification, clustering, segmentation, relationship discovery, and practically any task that discovers latent information from written natural language. Classical mining algorithms have traditionally focused on shallow representations such as bag-of-words and similar feature-based models. With the advent of modern high performance computing, deep sentence level linguistic analysis of large scale text corpora has become practical. In this dissertation, we evaluate the utility of dependency parses as textual features for different text mining applications. Dependency parsing is one form of syntactic parsing, based on the dependency grammar implicit in sentences. While dependency parsing has traditionally been used for text understanding, we investigate here its application to supply features for text mining applications. We specifically focus on three methods to construct textual features from dependency parses. First, we consider a dependency parse as a general feature akin to a traditional bag-of-words model. Second, we consider the dependency parse as the basis to build a feature graph representation. Finally, we use dependency parses in a supervised collocation mining method for feature selection. To investigate these three methods, several applications are studied, including: (i) movie spoiler detection, (ii) text segmentation, (iii) query expansion, and (iv) recommender systems. / Ph. D.

The application of feedback to enhance motivation is beneficial across various life contexts. While both feedback and motivation have been studied widely in psychological science, most of this research has used close-ended approaches to study feedback empirically, which limits the scope of investigation. The present study was one of the first applications of text-analysis to assess the impact of feedback on the recipient's motivation. A transformer machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Feedback was defined and evaluated from the perspective of Feedback Intervention Theory (FIT). Both research hypotheses were supported, given that the model's motivation predictions were positively associated with the actual motivation scores of feedback statements, and the model was closer to estimating the true motivation scores than expected by chance. These findings, paired with additional exploratory analyses, demonstrated the utility and effectiveness of the model in predicting perceived student motivation from feedback statements. Thus, this research provided a reliable tool researchers and practitioners in academia could use to evaluate the motivating influence of feedback for students, and it might inspire future studies in this domain. / Doctor of Philosophy / The use of feedback to enhance motivation is beneficial across various life domains. While both feedback and motivation have been studied widely in psychological science, most of this research has used close-ended (not text-analytic) approaches to study feedback empirically, which limits the scope of investigation. The present study was one of the first applications of text-analysis to assess the impact of feedback on the recipient's motivation. A machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Both research hypotheses were supported. The motivation predictions were positively associated with the actual motivation scores of feedback statements, and the model was closer to estimating the true motivation scores than would be expected by chance. These findings, paired with additional exploratory analyses, demonstrated the utility and effectiveness of the model in predicting perceived student motivation from feedback statements. Additionally, based on this study it is recommended that professors include specific behaviors to be modified when delivering feedback. Thus, this research provided a tool that researchers and practitioners in academia could use to evaluate the motivating influence of feedback for students, and it might certainly inspire future studies in this domain.

Into the Into of Earth Itself is a poetry collection that investigates the relationship between ecological violation and the violation of women, as well as toxicity and toxic masculinity. In doing so, it draws from the histories of two Pennsylvania towns: Palmerton and Centralia. The former is a Superfund site ravaged by zinc pollution and currently under threat of hydraulic fracturing and pipeline expansion. The latter is a nearby ghost town that was condemned and evacuated due to an underground mine fire, which will continue for another 200 years. The manuscript uses visual forms and digital text mining techniques to craft poetry about these extractive relationships to land and women. The speaker asks herself: As a woman, how have I also been mined and fracked by these same societal technologies? / Master of Fine Arts / Into the Into of Earth Itself is a poetry collection.

Målet med mitt arbete har varit att utvecklas som låtskrivare, främst inriktat på låttexter. Jag har gjort det genom att analysera 5 olika låttexter utifrån ett par förbestämda parametrar för att få en djupare förståelse för hur de teoretiska metoderna kan appliceras i praktiken. Jag har sedan skrivit 4 egna låttexter efter 4 olika tillvägagångssätt och sedan jämfört hur processen har sett ut i olika delar av låtskrivandet. I slutet av arbetet ansåg jag att det inte var särskilt stor skillnad på de olika tillvägagångssätten jag hade valt till just det här arbetet, men jag kom fram till att det förmodligen skulle vara större skillnad om jag hade valt andra tillvägagångssätt att jobba med.  Jag hoppas att det här arbetet kan ge inspiration till att verkligen tänka till på hur mycket påverkan en text kan ha när man skriver musik och att mitt arbete kan inspirera till en större djupdykning i just låttexter.

Research across healthcare and organizational settings demonstrates the importance of social support to increase physical and mental well-being. However, the process of seeking social support is less well-understood than its outcomes. Specifically, research examining how people seek social support in natural settings is scarce. One natural setting increasingly used by people to seek support is the internet. In this online setting, people seek and provide social support verbally via social media platforms and messages. The present project seeks to further examine the nature of social support seeking in these online contexts by examining people’s language. This analysis includes discovering the common language features of social support seeking. By applying a data-driven content analysis approach, this research can examine the underlying themes present when seeking social support and build upon that insight to classify new instances of support seeking. These results would have important practical implications for occupational health. By identifying individuals who are seeking social support, future interventions will be able to take a more targeted approach in lending additional support to those individuals who have the greatest need. Subsequently, this application potentially provides the mental and physical health benefits of social support. Therefore, this research extends our knowledge of both the nature of support seeking and how to develop effective interventions. / M.S. / Research suggests that social support has important effects on our mental and physical health. To this point, though, the process of seeking social support has largely been neglected in research. Specifically, there hasn’t been much research on how social support is sought online. We know that people seek social support online by posting and messaging on social media. The present study seeks to examine the language of online support seeking—this way, we can understand what people tend to say when seeking support. The present study is concerned with the content of support seeking posts; by analyzing this content, we can understand themes that are prevalent in online support seeking. This allows us to better understand support seeking and, hopefully, better identify people in need of support. By identifying those people in need of support, we can ensure that their support needs are met and that they don’t suffer the health consequences related to a lack of social support. Therefore, this research extends our knowledge of social support seeking, both theoretically and practically.

Content is generated on the web at an increasing rate. The type of content varies from text on a traditional webpage to text on social media portals (e.g., social network sites and microblogs). One such example of social media is the microblogging site Twitter. Twitter is known for its high level of activity during live events, natural disasters, and events of global importance. Challenges with the data in the Twitter universe include the limit of 140 characters on the text length. Because of this limitation, the vocabulary in the Twitter universe includes short abbreviations of sentences, emojis, hashtags, and other non-standard usage. Consequently, traditional text classification techniques are not very effective on tweets. Fortunately, sophisticated text processing techniques like cleaning, lemmatizing, and removal of stop words and special characters will give us clean text which can be further processed to derive richer word semantic and syntactic relationships using state of the art feature selection techniques like Word2Vec. Machine learning techniques, using word features that capture semantic and context relationships, can be of benefit regarding classification accuracy. Improving text classification results on Twitter data would pave the way to categorize tweets relative to human defined real world events. This would allow diverse stakeholder communities to interactively collect, organize, browse, visualize, analyze, summarize, and explore content and sources related to crises, disasters, human rights, inequality, population growth, resiliency, shootings, sustainability, violence, etc. Having the events classified into different categories would help us study causality and correlations among real world events. To check the efficacy of our classifier, we would compare our experimental results with an Association Rules (AR) classifier. This classifier composes its rules around the most discriminating words in the training data. The hierarchy of rules, along with an ability to tune to a support threshold, makes it an effective classifier for scenarios where short text is involved. Traditionally, developing classification systems for these purposes requires a great degree of human intervention. Constantly monitoring new events, and curating training and validation sets, is tedious and time intensive. Significant human capital is required for such annotation endeavors. Also, involved efforts are required to tune the classifier for best performance. Developing and tuning classifiers manually using human intervention would not be a viable option if we are to monitor events and trends in real-time. We want to build a framework that would require very little human intervention to build and choose the best among the available performing classification techniques in our system. Another challenge with classification systems is related to their performance with unseen data. For the classification of tweets, we are continually faced with a situation where a given event contains a certain keyword that is closely related to it. If a classifier, built for a particular event, due to overfitting to what is a biased sample with limited generality, is faced with new tweets with different keywords, accuracy may be reduced. We propose building a system that will use very little training data in the initial iteration and will be augmented with automatically labelled training data from a collection that stores all the incoming tweets. A system that is trained on incoming tweets that are labelled using sophisticated techniques based on rich word vector representation would perform better than a system that is trained on only the initial set of tweets. We also propose to use sophisticated deep learning techniques like Convolutional Neural Networks (CNN) that can capture the combination of the words using an n-gram feature representation. Such sophisticated feature representation could account for the instances when the words occur together. We divide our case studies into two phases: preliminary and final case studies. The preliminary case studies focus on selecting the best feature representation and classification methodology out of the AR and the Word2Vec based Logistic Regression classification techniques. The final case studies focus on developing the augmented semi-supervised training methodology and the framework to develop a large collection learning optimizer to generate a highly performant classifier. For our preliminary case studies, we are able to achieve an F1 score of 0.96 that is based on Word2Vec and Logistic Regression. The AR classifier achieved an F1 score of 0.90 on the same data. For our final case studies, we are able to show improvements of F1 score from 0.58 to 0.94 in certain cases based on our augmented training methodology. Overall, we see improvement in using the augmented training methodology on all datasets. / Master of Science / Content is generated on social media at a very fast pace. Social media content in the form of tweets that is generated by the microblog site Twitter is quite popular for understanding the events and trends that are prevalent at a given point of time across various geographies. Categorizing these tweets into their real-world event categories would be useful for researchers, students, academics and the government. Categorizing tweets to their real-world categories is a challenging task. Our framework involves building a classification system that can learn how to categorize tweets for a given category if it is provided with a few samples of the relevant and non-relevant tweets. The system retrieves additional tweets from an auxiliary data source to further learn what is relevant and irrelevant based on how similar a tweet is to a positive example. Categorizing the tweets in an automated way would be useful in analyzing and studying the events and trends for past and future real-world events.