An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

A Survey on Ambient-Assisted Living Tools for Older Adults

https://bura.brunel.ac.uk/bitstream/2438/9980/2/Fulltext.pdf

The Role of Text Pre-processing in Sentiment Analysis

任何一种描写语法都是以某种描写框架为基础的,不管这种框架如何不够完善,但它总是反映了描写的目标和作者的理论倾向.在《当代英语语法》(A Grammar of Contemporary English,以下简称GCE)中,Randolph Quirk, Sidney Greenbaum, Geoffrey Leech和Jan Svartvik等四位作者的目标是试图系统地、综合性地描写英语的表层结构,分析结构的语义及其在情境里的用法.通读全书后我们感到,这个目标并不依傍于任何一种语言理论,因为迄今为止每

谈谈A Grammar of Contemporary English对于分句与句子问题的若干处理

Causation relations are a pervasive feature of human language. Despite this, the automatic acquisition of causal information in text has proved to be a difficult task in NLP. This paper provides a method for the automatic detection and extraction of causal relations. We also present an inductive learning approach to the automatic discovery of lexical and semantic constraints necessary in the disambiguation of causal relations that are then used in question answering. We devised a classification of causal questions and tested the procedure on a QA system.

/pdf/automatic-detection-of-causal-relations-for-question-3wo93uffo0.pdf

Automatic Detection of Causal Relations for Question Answering

This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.

/pdf/extracting-causal-knowledge-from-a-medical-database-using-15bd8qgodk.pdf

Extracting causal knowledge from a medical database using graphical patterns

This paper reports a study in automatic sentiment classification, i.e., automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In the first part of this study, several different approaches, unigrams (individual words), selected words (such as verb, adjective, and adverb), and words labeled with part-of-speech tags were investigated. A sample of 1,800 various product reviews was retrieved from Review Centre (www.reviewcentre.com) for the study. 1,200 reviews were used for training, and 600 for testing. Using SVM, the baseline unigram approach obtained an accuracy rate of around 76%. The use of selected words obtained a marginally better result of 77.33%. Error analysis suggests various approaches for improving classification accuracy: use of negation phrase, making inference from superficial words, and solving the problem of comments on parts. The second part of the study that is in progress investigates the use of negation phrase through simple linguistic processing to improve classification accuracy. This approach increased the accuracy rate up to 79.33%.

https://dr.ntu.edu.sg/bitstream/10220/20045/1/na_sui_khoo.sentiment_classification.isko2004.pdf

Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews

This chapter presents a broad survey of the cause-effect relation, with particular emphasis on how the relation is expressed in text. Philosophers have been grappling with the concept of causation for centuries. Researchers in social psychology have found that the human mind has a very complex mechanism for identifying and attributing the cause for an event. Inferring cause-effect relations between events and statements has also been found to be an important part of reading and text comprehension, especially for narrative text. Though many of the cause-effect relations in text are implied and have to be inferred by the reader, there is also a wide variety of linguistic expressions for explicitly indicating cause and effect. In addition, it has been found that certain words have “causal valence”—they bias the reader to attribute cause in certain ways. Cause-effect relations can also be divided into several different types.

The Many Facets of the Cause-Effect Relation

In this paper, we present a new algorithm that automatically classifies wandering patterns (or behaviors) of patients with Alzheimer's disease and other different types of dementia. Experimental results on a real-life dataset show that this algorithm can provide a robust and credible assistive technology for monitoring patients with dementia (PWD) who are prone to wandering. Combined with indoor and outdoor location technologies using ubiquitous devices such as smart phones, we also demonstrate the feasibility of a remote mobile healthcare monitoring solution that is capable of reasoning about wandering behaviors of PWD and real-time detection of abnormalities that require timely intervention from caregivers.

Feasibility study of a real-time wandering detection algorithm for dementia patients

Purpose This study focuses on travel patterns of people with dementia (PWD), which can be classified as direct, pacing, lapping, or random based on the Martino-Saltzman (MS) model. Method We take the movement data of five nursing home residents with dementia, comprising 220 travel episodes of room-to-room movements, and manually applied MS model to classify the travel patterns in each episode. Next, we propose two approaches to automatically classify the travel patterns: a machine learning approach and a deterministic predefined tree-based algorithm. In the machine learning approach, eight classical algorithms including Naive Bayes (NB), Multilayer Perceptron (MLP), Pruned decision trees (C4.5), Random Forests (RF), Logiboost (LB) and Bagging (BAG) with pruned C4.5 trees as base classifiers, k-Nearest Neighbor (k-NN) with one neighbor, and Support Vector Machine (SVM) are employed. Results RF yields the best classification results. The sensitivity, specificity, precision, recall, F1-measure of the RF are 92.3%, 92.3%, 92.2%, 92.3%, 92.2% respectively. The best classification latency, which is 0.01s, is achieved by NB, C4.5, BAG, and k-NN. In the deterministic approach, we have developed a set of predefined tree-based algorithms to rectify the shortcomings of classical machine learning algorithms. Experimental results indicate that the deterministic algorithm is able to classify direct and various models of indirect travel with 98.2% sensitivity, 98.1% specificity, 98.2% precision, 98.2% recall, 98.2% F1-measure, and 0.0003s classification latency. Conclusion The deterministic algorithm is simple to implement and highly suitable for real time applications aiming to monitor wandering behavior of PWD in long term care settings.

Syin Chan

Papers

Extracting causal knowledge from a medical database using graphical patterns

Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews

The Many Facets of the Cause-Effect Relation

Feasibility study of a real-time wandering detection algorithm for dementia patients

Automated detection of wandering patterns in people with dementia