Big Data Analysis in Social Networks : Extracting Food Preferences of Vegans from Twitter

Market research is often conducted through conventional methods such as surveys, focus groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming. This study develops a new method, based on a combination of standard techniques like sentiment analysis and normalisation, to conduct market research in a manner that is free and quick. The method can be used in many application-areas, but this study focuses mainly on the veganism market to identify vegan food preferences in the form of a profile. Several food words are identified, along with their distribution between positive and negative sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and chicken dominate the profile, indicating that there is a significant market for vegan-suitable alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato, blueberries, kale and tofu also make strong appearances in the profile. Validation is performed by using the method on Volkswagen vehicle data to identify positive and negative sentiment across five car models. Some results were found to be consistent with sales figures and expert reviews, while others were inconsistent. The reliability of the method is therefore questionable, so the results should be used with caution.

Exploring associations between violent discipline and aggressive behavior in children ages 4-5 in The Republic of Nicaragua: A secondary data analysis of the 2011 Demographic and Health Survey

Thesis (Master's)--University of Washington, 2016-06

The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections

For a long time, electronic data analysis has been associated with quantitative methods. However, Computer Assisted Qualitative Data Analysis Software (CAQDAS) are increasingly being developed. Although the CAQDAS has been there for decades, very few qualitative health researchers report using it. This may be due to the difficulties that one has to go through to master the software and the misconceptions that are associated with using CAQDAS. While the issue of mastering CAQDAS has received ample attention, little has been done to address the misconceptions associated with CAQDAS. In this paper, the author reflects on his experience of interacting with one of the popular CAQDAS (NVivo) in order to provide evidence-based implications of using the software. The key message is that unlike statistical software, the main function of CAQDAS is not to analyse data but rather to aid the analysis process, which the researcher must always remain in control of. In other words, researchers must equally know that no software can analyse qualitative data. CAQDAS are basically data management packages, which support the researcher during analysis.

Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Heart failure following cancer therapy in patients with hematological malignancies aged 18 years and under : a linked health data analysis (1996-2009)

Introduction: The causal link between chemotherapy treatments and subsequent cardiotoxicity is well established, particularly for children with hematological malignancies. Little information exists on the characteristics and outcomes for patients with heart failure (HF) after chemotherapy. This study aimed to describe the characteristics, survival and mortality of patients who received chemotherapy for hematological cancer (leukemias, lymphomas and related disorders) before 18 years old and subsequently developed HF compared to those who did not.

Methods: Linked health data (1996-2009) from the Queensland Cancer Registry, Death Registry and Hospital Administration records for HF and chemotherapy admissions were reviewed. From all breast and hematological cancers patients (n=73,158), 15,987 received chemotherapy, including 819 patients aged ≤18 years at time of cancer diagnosis. Patients were categorized as those with an index HF admission (occurred after cancer diagnosis) and those without an index HF admission (non HF).

Results: Of the 819 patients, 3.7% (n=30) had an index HF admission. Median age of HF patients at time of cancer diagnosis was 5 years (IQR 3-12) compared to 7 years (IQR 3-14) in the non HF group (p=0.503). Median follow up from cancer diagnosis was 2.5 years in the HF group compared to 5.42 years in the non HF group (p<0.01). Of those who developed HF, 70% (n=21) had the index admission within 12 months of their cancer diagnosis. Of those with HF, 53.3% (n=16) died (all cause) compared to 14.6% (n=115) with no HF. On adjustment for age, sex and chemotherapy admissions, HF patients had an almost 5 fold increased mortality risk compared to non HF patients (HR 4.91 [95% CI, 2.88-8.36]) (Figure 1).

Conclusions: This study demonstrated that in children with hematological cancers the onset of HF occurred soon after chemotherapy and mortality risk is almost 5 times that of children who do not develop HF. Innovative strategies are still needed for the prevention and management of cardiotoxicity in this population.

Sustainable subdivisions – ventilation data analysis

This project, as part of a broader Sustainable Sub-divisions research agenda, addresses the role of natural ventilation in reducing the use of energy required to cool dwellings

Safeguarding Rural Australia : Introduction, Framework and Background to Secondary Data Analysis

This report provides an introduction to our analyses of secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution. Firstly, a wide and inclusive view of violence which encompasses measures of violent acts and incidents and also data identifying risk taking behaviour and the consequences of violence is outlined and justified. Secondly, the classification used to make comparisons between the city and the bush together with associated caveats is outlined. The third element discussed is in relation to national injury data. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in five subsequent reports in this series.

Study of acoustic emission data analysis tools for structural health monitoring applications

Acoustic emission (AE) technique is one of the popular diagnostic techniques used for structural health monitoring of mechanical, aerospace and civil structures. But several challenges still exist in successful application of AE technique. This paper explores various tools for analysis of recorded AE data to address two primary challenges: discriminating spurious signals from genuine signals and devising ways to quantify damage levels.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.