Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Seizure detection in subdural recordings using nonlinear decision functions

This dissertation proposed a new approach to seizure detection in intracranial EEG recordings using nonlinear decision functions. It implemented well-established features that were designed to deal with complex signals such as brain recordings, and proposed a 2-D domain of analysis. Since the features considered assume both the time and frequency domains, the analysis was carried out both temporally and as a function of different frequency ranges in order to ascertain those measures that were most suitable for seizure detection. In retrospect, this study established a generalized approach to seizure detection that works across several features and across patients. ^ Clinical experiments involved 8 patients with intractable seizures that were evaluated for potential surgical interventions. A total of 35 iEEG data files collected were used in a training phase to ascertain the reliability of the formulated features. The remaining 69 iEEG data files were then used in the testing phase. ^ The testing phase revealed that the correlation sum is the feature that performed best across all patients with a sensitivity of 92% and an accuracy of 99%. The second best feature was the gamma power with a sensitivity of 92% and an accuracy of 96%. In the frequency domain, all of the 5 other spectral bands considered, revealed mixed results in terms of low sensitivity in some frequency bands and low accuracy in other frequency bands, which is expected given that the dominant frequencies in iEEG are those of the gamma band. In the time domain, other features which included mobility, complexity, and activity, all performed very well with an average a sensitivity of 80.3% and an accuracy of 95%. ^ The computational requirement needed for these nonlinear decision functions to be generated in the training phase was extremely long. It was determined that when the duration dimension was rescaled, the results improved and the convergence rates of the nonlinear decision functions were reduced dramatically by more than a 100 fold. Through this rescaling, the sensitivity of the correlation sum improved to 100% and the sensitivity of the gamma power to 97%, which meant that there were even less false negatives and false positives detected. ^

Nonlinear optical effects manifested by electromagnetic induced transparency in cold atoms

This dissertation reports experimental studies of nonlinear optical effects manifested by electromagnetically induced transparency (EIT) in cold Rb atoms. The cold Rb atoms are confined in a magneto-optic trap (MOT) obtained with the standard laser cooling and trapping technique. Because of the near zero Doppler shift and a high phase density, the cold Rb sample is well suited for studies of atomic coherence and interference and related applications, and the experiments can be compared quantitatively with theoretical calculations. It is shown that with EIT induced in the multi-level Rb system by laser fields, the linear absorption is suppressed and the nonlinear susceptibility is enhanced, which enables studies of nonlinear optics in the cold atoms with slow photons and at low light intensities. Three independent experiments are described and the experimental results are presented. First, an experimental method that can produce simultaneously co-propagating slow and fast light pulses is discussed and the experimental demonstration is reported. Second, it is shown that in a three-level Rb system coupled by multi-color laser fields, the multi-channel two-photon Raman transitions can be manipulated by the relative phase and frequency of a control laser field. Third, a scheme for all-optical switching near single photon levels is developed. The scheme is based on the phase-dependent multi-photon interference in a coherently coupled four-level system. The phase dependent multi-photon interference is observed and switching of a single light pulse by a control pulse containing ∼20 photons is demonstrated. These experimental studies reveal new phenomena manifested by quantum coherence and interference in cold atoms, contribute to the advancement of fundamental quantum optics and nonlinear optics at ultra-low light intensities, and may lead to the development of new techniques to control quantum states of atoms and photons, which will be useful for applications in quantum measurements and quantum photonic devices.

Lattice Boltzmann method for flow and heat transfer in microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

An automated method for the determination of electroreception thresholds in weakly electric fish

Weakly electric fish produce a dual function electric signal that makes them ideal models for the study of sensory computation and signal evolution. This signal, the electric organ discharge (EOD), is used for communication and navigation. In some families of gymnotiform electric fish, the EOD is a dynamic signal that increases in amplitude during social interactions. Amplitude increase could facilitate communication by increasing the likelihood of being sensed by others or by impressing prospective mates or rivals. Conversely, by increasing its signal amplitude a fish might increase its sensitivity to objects by lowering its electrolocation detection threshold. To determine how EOD modulations elicited in the social context affect electrolocation, I developed an automated and fast method for measuring electroreception thresholds using a classical conditioning paradigm. This method employs a moving shelter tube, which these fish occupy at rest during the day, paired with an electrical stimulus. A custom built and programmed robotic system presents the electrical stimulus to the fish, slides the shelter tube requiring them to follow, and records video of their movements. I trained the electric fish of the genus Sternopygus was trained to respond to a resistive stimulus on this apparatus in 2 days. The motion detection algorithm correctly identifies the responses 91% of the time, with a false positive rate of only 4%. This system allows for a large number of trials, decreasing the amount of time needed to determine behavioral electroreception thresholds. This novel method enables the evaluation the evolutionary interplay between two conflicting sensory forces, social communication and navigation.

Nonlinear quantum transport in low-dimensional electronic devices

The study of transport processes in low-dimensional semiconductors requires a rigorous quantum mechanical treatment. However, a full-fledged quantum transport theory of electrons (or holes) in semiconductors of small scale, applicable in the presence of external fields of arbitrary strength, is still not available. In the literature, different approaches have been proposed, including: (a) the semiclassical Boltzmann equation, (b) perturbation theory based on Keldysh's Green functions, and (c) the Quantum Boltzmann Equation (QBE), previously derived by Van Vliet and coworkers, applicable in the realm of Kubo's Linear Response Theory (LRT). ^ In the present work, we follow the method originally proposed by Van Wet in LRT. The Hamiltonian in this approach is of the form: H = H 0(E, B) + λV, where H0 contains the externally applied fields, and λV includes many-body interactions. This Hamiltonian differs from the LRT Hamiltonian, H = H0 - AF(t) + λV, which contains the external field in the field-response part, -AF(t). For the nonlinear problem, the eigenfunctions of the system Hamiltonian, H0(E, B), include the external fields without any limitation on strength. ^ In Part A of this dissertation, both the diagonal and nondiagonal Master equations are obtained after applying projection operators to the von Neumann equation for the density operator in the interaction picture, and taking the Van Hove limit, (λ → 0, t → ∞, so that (λ2 t)n remains finite). Similarly, the many-body current operator J is obtained from the Heisenberg equation of motion. ^ In Part B, the Quantum Boltzmann Equation is obtained in the occupation-number representation for an electron gas, interacting with phonons or impurities. On the one-body level, the current operator obtained in Part A leads to the Generalized Calecki current for electric and magnetic fields of arbitrary strength. Furthermore, in this part, the LRT results for the current and conductance are recovered in the limit of small electric fields. ^ In Part C, we apply the above results to the study of both linear and nonlinear longitudinal magneto-conductance in quasi one-dimensional quantum wires (1D QW). We have thus been able to quantitatively explain the experimental results, recently published by C. Brick, et al., on these novel frontier-type devices. ^

Commitment language as a predictor of alcohol and marijuana use outcomes among adolescents in school -based group treatment

Despite widespread recognition of the problem of adolescent alcohol and other drug (AOD) abuse, research on its most common treatment modality, group work, is lacking. This research gap is alarming given that outcomes range from positive to potentially iatrogenic. This study sought to identify change mechanisms and/or treatment factors that are observable within group treatment sessions and that may predict AOD use outcomes. This NIH (F31 DA 020233-01A1) study evaluated 108, 10-19 year olds and the 19 school-based treatment groups to which they were previously assigned (R01 AA10246; PI: Wagner). Associations between motivational interviewing (MI) based change talk variables, group leader MI skills, and alcohol and marijuana use outcomes up to 12-months following treatment were evaluated. Treatment session audio recordings and transcripts (1R21AA015679-01; PI: Macgowan) were coded using a new discourse analysis coding scheme for measuring group member change talk (Amrhein, 2003). Therapist MI skills were similarly measured using the Motivational Interviewing Treatment Integrity instrument. Group member responses to commitment predicted group marijuana use at the 1-month follow up. Also, group leader empathy was significantly associated with group commitment for marijuana use at the middle and ending stages of treatment. Both of the above process measures were applied in a group setting for the first time. Building upon MI and social learning theory principles, group commitment and group member responses to commitment are new observable, in-session, process constructs that may predict positive and negative adolescent group treatment outcomes. These constructs, as well as the discourse analysis method and instruments used to measure them, raise many possibilities for future group work process research and practice.

Lattice Boltzmann Method for Flow and Heat Transfer in Microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

Nonlinear quantum transport in low-dimensional electronic devices

The study of transport processes in low-dimensional semiconductors requires a rigorous quantum mechanical treatment. However, a full-fledged quantum transport theory of electrons (or holes) in semiconductors of small scale, applicable in the presence of external fields of arbitrary strength, is still not available. In the literature, different approaches have been proposed, including: (a) the semiclassical Boltzmann equation, (b) perturbation theory based on Keldysh's Green functions, and (c) the Quantum Boltzmann Equation (QBE), previously derived by Van Vliet and coworkers, applicable in the realm of Kubo's Linear Response Theory (LRT). In the present work, we follow the method originally proposed by Van Vliet in LRT. The Hamiltonian in this approach is of the form: H = H°(E, B) + λV, where H0 contains the externally applied fields, and λV includes many-body interactions. This Hamiltonian differs from the LRT Hamiltonian, H = H° - AF(t) + λV, which contains the external field in the field-response part, -AF(t). For the nonlinear problem, the eigenfunctions of the system Hamiltonian, H°(E, B) , include the external fields without any limitation on strength. In Part A of this dissertation, both the diagonal and nondiagonal Master equations are obtained after applying projection operators to the von Neumann equation for the density operator in the interaction picture, and taking the Van Hove limit, (λ → 0 , t → ∞ , so that (λ2 t)n remains finite). Similarly, the many-body current operator J is obtained from the Heisenberg equation of motion. In Part B, the Quantum Boltzmann Equation is obtained in the occupation-number representation for an electron gas, interacting with phonons or impurities. On the one-body level, the current operator obtained in Part A leads to the Generalized Calecki current for electric and magnetic fields of arbitrary strength. Furthermore, in this part, the LRT results for the current and conductance are recovered in the limit of small electric fields. In Part C, we apply the above results to the study of both linear and nonlinear longitudinal magneto-conductance in quasi one-dimensional quantum wires (1D QW). We have thus been able to quantitatively explain the experimental results, recently published by C. Brick, et al., on these novel frontier-type devices.