Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

How to Do Things With Words

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area.

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Learning Word Vectors for Sentiment Analysis

Sentiment analysis algorithms and applications: A survey

Background China's One Child Policy (OCP), enforced in 1980, was the largest fertility control policy in human history, and its effects are still widely debated. Previous analyses of the OCP have focused on population reduction and sex ratios, among others. This study evaluates the impact of the OCP on infant mortality in China using quasi-experimental methods. Methods Country-level panel data for China and 106 other countries were extracted from the World Bank data repository for the years 1970-1989. The primary outcome was infant mortality rate (IMR) defined as deaths among children <1 year of age per 1000 live births. The impact of the OCP was estimated using uncontrolled and controlled interrupted time series (ITS) designs that control for within group time varying characteristics. Models were further adjusted for a comprehensive set of covariates and corrected for first-order autocorrelation. Countries included in the control groups were identified using the method of synthetic controls and propensity score matching (PSM). Valid control groups reduced threats from selection bias and unobserved time invariant confounders. Results Adjusting for GDP per capita, GDP growth, women's education, and population density, IMR decreased on average by 2.5 deaths per 1,000 annually before the OCP was enforced in China. The pre-OCP trends in IMR moved in tandem across China, synthetic and PSM controls, validating the parallel trends assumption. After the OCP, the trend in IMR in China increased by 2.24 (95% CI 1.39-3.09) per year, compared to the IMR trend in the pre-OCP period, comparing differences in China to synthetic controls. Results were similar and confirmed across PSM (1.96, 0.56-3.37) and uncontrolled ITS (2.14, 1.39-2.89) models. No significant immediate level change in IMR was observed in the controlled ITS models. Conclusions The reduction in IMR slowed down significantly after the OCP was enforced in China. The slowdown is likely due to a relative increase in the ratio of the IMR of females to males suggesting that practices such as female infanticide, abandonment, and neglect, stemming from a strong son preference were primary contributors. Coercive policies to reduce fertility can have unintended consequences.

The impact of the one child policy on China's infant mortality from 1970-1989: A quasi-experimental study

Abstract Monitoring online customer reviews is important for business organizations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.1

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Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

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The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Assessing student answers and providing valuable feedback is crucial for effective learning, but it can be a time-consuming task. Traditional methods of automating student answer assessment through text classification often suffer from issues such as lack of trustworthiness, transparency, and the ability to provide a rationale for the automated assessment process. These limitations hinder their usefulness in practice. In this paper, we explore using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation under both the zero-shot and few-shot settings. We introduce a critic module which automatically filters incorrect outputs from ChatGPT and utilizes the remaining ChtaGPT outputs as noisy labelled data to fine-tune a smaller language model, enabling it to perform student answer scoring and rationale generation. Moreover, by drawing multiple samples from ChatGPT outputs, we are able to compute predictive confidence scores, which in turn can be used to identify corrupted data and human label errors in the training set. Our experimental results demonstrate that despite being a few orders of magnitude smaller than ChatGPT, the fine-tuned language model achieves better performance in student answer scoring. Furthermore, it generates more detailed and comprehensible assessments than traditional text classification methods. Our approach provides a viable solution to achieve explainable automated assessment in education.

Distilling ChatGPT for Explainable Automated Student Answer Assessment

Text classifiers built on Pre-trained Language Models (PLMs) have achieved remarkable progress in various tasks including sentiment analysis, natural language inference, and question-answering. However, the occurrence of uncertain predictions by these classifiers poses a challenge to their reliability when deployed in practical applications. Much effort has been devoted to designing various probes in order to understand what PLMs capture. But few studies have delved into factors influencing PLM-based classifiers' predictive uncertainty. In this paper, we propose a novel framework, called CUE, which aims to interpret uncertainties inherent in the predictions of PLM-based models. In particular, we first map PLM-encoded representations to a latent space via a variational auto-encoder. We then generate text representations by perturbing the latent space which causes fluctuation in predictive uncertainty. By comparing the difference in predictive uncertainty between the perturbed and the original text representations, we are able to identify the latent dimensions responsible for uncertainty and subsequently trace back to the input features that contribute to such uncertainty. Our extensive experiments on four benchmark datasets encompassing linguistic acceptability classification, emotion classification, and natural language inference show the feasibility of our proposed framework. Our source code is available at: https://github.com/lijiazheng99/CUE.

Yulan He

Papers

The impact of the one child policy on China's infant mortality from 1970-1989: A quasi-experimental study

Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Distilling ChatGPT for Explainable Automated Student Answer Assessment

CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models