Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 48 for “"Topic model"”.
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A temporal topic model for social trend prediction
… content-based and user-centric prediction models where the objective is to employ Twitter content to predict whether the rates increase or decrease for the prospective time-frame. In order to collect Twitter data, we developed an activity-based sampling approach to collect credible users. …
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Topic Model-based Mass Spectrometric Data Analysis in Cancer Biomarker Discovery Studies
… the purification problem through probabilistic modeling. We propose an intensity-level purification model (IPM) to computationally purify LC/GC-MS based cancerous data in biomarker discovery studies. We further extend IPM to scan-level purification model (SPM) by considering information from …
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A topic model based approach to inferring episodic directional selection in protein coding sequences
… it varies between genomic loci. Most previous models have ignored or inadequately addressed some of these phenomena. This work extends recent approaches to modelling episodic directional selection acting on protein-coding sequences. We use inference techniques within the topic model framework …
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Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals
… state of the brain. We explore the use of topic modeling – which are popular text processing algorithms – to infer brain states from EEG signals. Latent Dirichlet allocation (LDA) is our preferred topic model because of its mixture-of-mixtures nature and its ability to be trained in an …
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Automatic detection of research interest using topic modeling
… research papers written by him or her, using a topic model learned from a corpus of research paper text not necessarily related to any faculty members of MIT, and a list of topic keywords such as that of the Library of Congress. The topic model was generated using a variant of Latent Dirichlet …
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SELF-ENHANCED VOCABULARY LEARNING LATENT DIRECHLET ALLOCATION
… sentiments and classifications. In the latter, topics models such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and Biterm Topic Model (BTM) (Yan et al., 2013) are conventional probabilistic models designed to unveil latent topic structure within texts. In the paper, we proposed a …
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Topic models for short text data
Topic models are known to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form …
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Large Scale Online Aggregation Via Distributed Systems
… three projects I have worked on under this topic. In the first project, I consider extending Online Aggregation (OLA) to a MapReduce environment. Online aggregation (OLA) allows the user to compute an arbitrary aggregation function over a data set and output probabilistic bounds on accuracy …
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Contextual text mining
… authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of …
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Clustering based causal topic mining
… timestamped document collection in the form of topics that cause a change in a time-series. We develop a conceptual framework for that can be used to analyze different causal topic mining algorithms. We also propose two novel clustering based algorithms - cCTM-CF and cCTM-CoF to generate causal …
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Bayesian nonparametric learning for complicated text mining
… value. It is commonly accepted that Bayesian models with finite-dimensional probability distributions as building blocks, also known as parametric topic models, are effective tools for text mining. However, one problem in existing parametric topic models is that the hidden topic number needs …
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Automatic identification of representative content on Twitter
… participants of discourse on a wide range of topics. As a consequence, Twitter has become an important part of the political battleground that journalists and political analysts can harness to analyze and understand the narratives that organically form, spread and decline among the public in a …
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Non-parametric bayesian methods for structured topic models
… several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to …
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Non-parametric bayesian methods for structured topic models
… several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to …
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Prediction and analysis of degree of suicidal ideation in online content
… to quality mental health care. Recently such models have been applied to online data, such as social media postings to augment mental health screening. Despite the potential of these methods, online ML classifiers still perform poorly in multi-class settings. In this thesis, we propose the …
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Quantifying Changes in Social Polarization Over Time and Region
… regional differences in the discussion of issues/topics. Our modeling approach employs the Structural Topic Model (STM) to identify topics within a given corpus and measure the tonal differences of articles discussing the same topic. Specifically, we use the STM to infer potentially related …
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News and financial market
… market. The proposed algorithm is based on topic model and adjust TF-IDF weighting. It allows us to identify a few factors that could influence the performance of a prediction algorithm, such as number of topics of a model and adjustment. of IDF value. Our experiment results also show that …
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"Don't Destroy Our Neighborhood": Neighborhood Imaginaries and the Politics of Upzoning / Displacement through Development: Spatial and Temporal Dynamics of Residential Eviction and Capital Investment
… evictions immediately precede demolition and remodeling activity. Additionally, permit activity in the surrounding neighborhood is associated with an increased risk of eviction, pointing to the role of broader neighborhood change in patterns of residential displacement.
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Content modeling for social media text
… content. This structure enables us to model the connection between individual documents and effectively aggregate their content. The models I propose demonstrate that content structure can be utilized at both document and phrase level to aid in standard text analysis tasks. At the …
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Robustness and generalization guarantees for statistical learning of generative models
… typically phrased in the context of generative models. By combining standard methods based on the theory of empirical processes with ideas from optimal transport and signal recovery, we formally address the generalization and robustness guarantees for the existing and newly suggested algorithms. …
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