Sentiment analysis of patient feedback

The application of sentiment analysis as a method for the automatic categorisation of opinions in text has grown increasingly popular across a number of domains over the past few years. In particular, health services have started to consider sentiment analysis as a solution for the task of processing the ever-growing amount of feedback that is received in regards to patient care. However, the domain is relatively under-studied in regards to the application of the technology, and the effectiveness and performance of methods have not been substantially demonstrated. Beginning with a survey of sentiment analysis and an examination of the work undertaken so far in the clinical domain, this thesis examines the application of supervised machine learning models to the classification of sentiment in patient feedback. As a starting point, this requires a suitably annotated patient feedback dataset, for both analysis and experimentation. Following the construction and detailed analysis of such a resource, a series of machine learning experiments study the impact of different models, features and review types to the problem. These experiments examine the applicability of the selected methods and demonstrate that model and feature choice may not be a significant issue in sentiment classification, whereas the type of review that the models train and test across does affect the outcome of classification. Finally, by examining the role that responses play in the patient feedback process and developing the idea of incorporating the inter-document context provided by the response into the feedback classification process, a recalibration framework for [continued…

Bayesian Optimization of Catalysts With In-context Learning

Large language models (LLMs) are able to do accurate classification with zero or only a few examples (in-context learning). We show a prompting system that enables regression with uncertainty for in-context learning with frozen LLM (GPT-3, GPT-3.5, and GPT-4) models, allowing predictions without features or architecture tuning. By incorporating uncertainty, our approach enables Bayesian optimization for catalyst or molecule optimization using natural language, eliminating the need for training or simulation. Here, we performed the optimization using the synthesis procedure of catalysts to predict properties. Working with natural language mitigates difficulty synthesizability since the literal synthesis procedure is the model's input. We showed that in-context learning could improve past a model context window (maximum number of tokens the model can process at once) as data is gathered via example selection, allowing the model to scale better. Although our method does not outperform all baselines, it requires zero training, feature selection, and minimal computing while maintaining satisfactory performance. We also find Gaussian Process Regression on text embeddings is strong at Bayesian optimization. The code is available in our GitHub repository: https://github.com/ur-whitelab/BO-LIF

The Effects of COVID-19 on Refugees in Peninsular Malaysia: Surveillance, Securitization, and Eviction

This paper focuses on the largest group of refugees in Malaysia, the Rohingya. Many Rohingya have made Malaysia their home over recent years, even though they have no official legal status in the country. Refugees more broadly are often tolerated as workers but treated as undocumented migrants by the law. When Covid-19 was detected in Malaysia, the government followed a strategy of suppression with targeted lockdowns in areas of Covid-19 outbreaks. As most refugees are forced to work to survive, they hold important front-line jobs. As a result, they were exposed to Covid-19 at higher rates of infection than Malaysians. In this paper we trace the way the Malaysian government, Malaysian people and refugees encountered Covid-19 and how refugees especially became the subject of enhanced securitization and surveillance based on prejudice. We show how the state enacted securitization first on the borders, before it inverted this process and focused on domestic border work, wherein neighborhoods, mosques and markets became central places of immigration control and exclusion for refugees. Based on data collected during ethnographic fieldwork in peninsular Malaysia between 2020 and 2021, we argue that the securitization of refugees and migrant workers, their surveillance and even expulsion and eviction demonstrates continued and heightened scapegoating of refugees and migrants for all Malaysia’s ills. These actions reinforced the stigma and stereotype of refugees being legally undocumented and therefore outside of and too often unwelcome in the Malaysian body politic

Social media measurement and monitoring

This chapter highlights the role that social media measurement and monitoring (SMM) processes can play in influencing business outcomes based on consumer behaviour analysis. It classifies the performance metrics into three groups, namely: Reach helps focus on the probable size of the user base, based on the number of unique impressions, followers, shares, comments and likes. The social media measurement and monitoring process is carried out to measure and monitor social media activities so an organization can achieve its goals. The process begins with a social media audit to assess how efficiently and effectively the brand's existing social media efforts are performing, as well as evaluating where the consumers are engaging and communicating. Mapping of objectives is carried out to understand the direction of the brand with regard to its efforts on social media and what it aims to achieve