Brand User Attention Model Based on Online Text Reviews: An Empirical Study of New Energy Automobile Brands

Accurately grasping the rules of user behavior and market changes and timely adjusting decisions and strategies are the ways for brand development and innovation. In this paper, we proposed a brand user attention model based on online text review analysis. First of all, we collected and preprocessed the user comment text from the online forum. Secondly, through the LDA topic model and LDAvis visual analysis, the potential topics of user reviews were extracted, and a multi-dimensional feature analysis model was constructed to reveal the users\u27 attention features of brand products. Finally, took the new energy automobile brands as an example, the users\u27 attention features for the different new energy automobile brands were explored and the empirical study was carried out. This study found that the brand user attention model based on online text analysis can effectively extract the characteristics that brand users care about, obtain valuable business insight, and provide support for managers\u27 decision-making

Metric for seleting the number of topics in the LDA Model

The latest technological trends are driving a vast and growing amount of textual data. Topic modeling is a useful tool for extracting information from large corpora of text. A topic template is based on a corpus of documents, discovers the topics that permeate the corpus and assigns documents to those topics. The Latent Dirichlet Allocation (LDA) model is the main, or most popular, of the probabilistic topic models. The LDA model is conditioned by three parameters: two Dirichlet hyperparameters (α and β ) and the number of topics (K). Determining the parameter K is extremely important and not extensively explored in the literature, mainly due to the intensive computation and long processing time. Most topic modeling methods implicitly assume that the number of topics is known in advance, thus considering it demands an exogenous parameter. That is annoying, leaving the technique prone to subjectivities. The quality of insights offered by LDA is quite sensitive to the value of the parameter K, and perhaps an excess of subjectivity in its choice might influence the confidence managers put on the techniques results, thus undermining its usage by firms. This dissertation’s main objective is to develop a metric to identify the ideal value for the parameter K of the LDA model that allows an adequate representation of the corpus and within a tolerable elapsed time of the process. We apply the proposed metric alongside existing metrics to two datasets. Experiments show that the proposed method selects a number of topics similar to that of other metrics, but with better performance in terms of processing time. Although each metric has its own method for determining the number of topics, some results are similar for the same database, as evidenced in the study. Our metric is superior when considering the processing time. Experiments show this method is effective.As tendências tecnológicas mais recentes impulsionam uma vasta e crescente quantidade de dados textuais. Modelagem de tópicos é uma ferramenta útil para extrair informações relevantes de grandes corpora de texto. Um modelo de tópico é baseado em um corpus de documentos, descobre os tópicos que permeiam o corpus e atribui documentos a esses tópicos. O modelo de Alocação de Dirichlet Latente (LDA) é o principal, ou mais popular, dos modelos de tópicos probabilísticos. O modelo LDA é condicionado por três parâmetros: os hiperparâmetros de Dirichlet (α and β ) e o número de tópicos (K). A determinação do parâmetro K é extremamente importante e pouco explorada na literatura, principalmente devido à computação intensiva e ao longo tempo de processamento. A maioria dos métodos de modelagem de tópicos assume implicitamente que o número de tópicos é conhecido com antecedência, portanto, considerando que exige um parâmetro exógeno. Isso é um tanto complicado para o pesquisador pois acaba acrescentando à técnica uma subjetividade. A qualidade dos insights oferecidos pelo LDA é bastante sensível ao valor do parâmetro K, e pode-se argumentar que um excesso de subjetividade em sua escolha possa influenciar a confiança que os gerentes depositam nos resultados da técnica, prejudicando assim seu uso pelas empresas. O principal objetivo desta dissertação é desenvolver uma métrica para identificar o valor ideal para o parâmetro K do modelo LDA que permita uma representação adequada do corpus e dentro de um tempo de processamento tolerável. Embora cada métrica possua método próprio para determinação do número de tópicos, alguns resultados são semelhantes para a mesma base de dados, conforme evidenciado no estudo. Nossa métrica é superior ao considerar o tempo de processamento. Experimentos mostram que esse método é eficaz

Exploring the Utility of Patient Stories on Social Media for Healthcare Quality Improvement

This thesis explores the phenomenon of patient stories on social media. This phenomenon represents the intersection of two phenomena: patient experience and social media. Healthcare experience refers to the interactions of a patient with the healthcare system members, including the nurses, physicians, and staff, and the resultant emotional and behavioural effects of these interactions on patients, including patient satisfaction, patient commitment to health, and patient adherence to treatment plans. Social media refers to the internet-based applications that enable people to communicate, interact, publish, and exchange all types and formats of information, including text, pictures, audio, and video. Patient stories on social media refer to patients' posts that describe their healthcare experiences. This thesis aims to assess the utility of patient stories on social media for healthcare quality improvement and explore the health system and policy factors that can positively or negatively affect this utility in the healthcare system in Ontario. The thesis is comprised of an introduction chapter, a theoretical perspective chapter, four studies presented in chapters 3 to 6, and a conclusion chapter. Additional material is provided in several appendixes, including a definitions section in Appendix 1.A. The first study seeks to understand the perspectives of healthcare providers and administrators in Ontario regarding the factors affecting the patient experience. Qualitative data were collected between April 2018 and May 2019 by interviewing 21 healthcare providers and administrators in Ontario. Interviewees included physicians, nurses, optometrists, dietitians, quality managers, and policymakers. The study findings show that there are two perspectives on patient experience: the biomedical perspective, which prioritizes health outcomes and gives high weights to healthcare experience factors that can be controlled by healthcare providers, while ignoring other factors, and the sociopolitical perspective, which recognizes the impacts of healthcare politics and the social context of health on patient experience in Ontario. The second study explores the perspectives of healthcare providers and administrators on patient stories on social media and whether they can be used for evaluating healthcare experiences. Data were collected between April 2018 and May 2019 by interviewing the 21 healthcare providers, and administrators in Ontario noted in study one. Study findings show that several barriers prevent healthcare providers from realizing the benefits of social media, including the professional healthcare standards and codes of conduct, the time and effort required to process these stories, and the significant number of stories on social media, which also increase the time needed to process these stories. The third study analyzes the social media policies of the healthcare regulatory authorities, which are the regulating and licensing bodies in Ontario, and explores how these policies encourage or discourage the use of social media by healthcare providers. The study uses document analysis and qualitative content analysis to analyze social media policies and guidelines of some healthcare colleges in Ontario issued between 2013 and 2019. The study findings show that in the healthcare system in Ontario, social media is perceived as a source of risks to the healthcare professions and professionals, and therefore, policies are developed to mitigate those risks. Healthcare regulators emphasize that the codes of conduct and the professional standards of healthcare extend to social media, despite the distinct context of social media. The study found no systematic recognition of patient stories on social media as a source of information that requires the attention of healthcare professionals. The fourth study analyzes patient stories on the Care Opinion platform, which is an online platform that enables patients to post stories about their healthcare experiences and enables the providers to respond to these stories. The study explores the elements of healthcare experience in these stories, the characteristics of the stories that receive responses from healthcare providers, and the association between the satisfaction level of the patient expressed in these stories and the likelihood of receiving a provider response. The study collected 367,573 patient stories from the Care Opinion platform that were posted between September 2005 to September 2019. The study uses topic modelling (Latent Dirichlet Allocation), sentiment analysis, and logistic regression to analyze the data. Data analysis identified 16 topics in these stories. These topics can be grouped into five categories: communication, quality of clinical services, quality of services, human aspects of healthcare experiences, and patient satisfaction. Stories that describe healthcare experience of a family member, or reflect patient thankfulness, gratitude, or satisfaction with communication are associated with a high likelihood of receiving a provider’s response; however; the sentiment score of a story, which I used as a proxy for patient satisfaction, was insignificant. The thesis concludes by identifying several barriers that impede the use of patient stories on social media for quality improvement. These barriers are the beliefs and priorities of healthcare providers, the social media policies of the healthcare regulatory authorities and professional healthcare standards and codes of conduct that restrict patient-provider communication, the time and effort required to process patient stories, and the credibility of patient stories

What impacts matriculation decisions? A double-blind experiment via an AI-led chatbot trained with social media data

This thesis explores students’ matriculation decision factors via an AI-led chatbot trained with social media data. The novelty of this thesis resides in the following methodological approaches: Firstly, it employs data mining and text analytics techniques to explore the use of topic modelling and a systematic literature reviewing technique called algorithmic document sequencing to identify decision factors from social media to be integrated to the internal model of the AI through a methodological pluralist approach. Secondly, it introduces a chatbot design and strategy for an AI-led chat survey generating both unstructured qualitative and structured quantitative primary data. Finally, upon interviewing 1193 participants around the world, a double-blind true experiment was run seamlessly without human intervention by the AI testing hypotheses and determining the factors that impact students' university choices. The thesis showcases how AI can efficiently interview participants and collect their input, offering robust evidence through an RCT (Gold standard) to establish causal relationships between interventions and their outcomes. One significant contribution of the thesis lies in aiding higher education institutions in understanding the global factors influencing students' university choices and the role of electronic word-of-mouth on social media platforms. More importantly, the research enhances knowledge in identifying themes from social media and literature, facilitating the training of AI-augmented chatbots with these themes, and designing such chatbots to run large scale social RCTs. These developments may enable researchers from a wide range of fields to collect qualitative and quantitative data from large samples, run double-blind true experiments with the AI and produce statistically reproducible, reliable, and generalisable results