Crowdsourcing a Word-Emotion Association Lexicon

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word-emotion and word-polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher inter-annotator agreement than that obtained by asking if a term evokes an emotion

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Online suicide prevention through optimised text classification

Online communication platforms are increasingly used to express suicidal thoughts. There is considerable interest in monitoring such messages, both for population-wide and individual prevention purposes, and to inform suicide research and policy. Online information overload prohibits manual detection, which is why keyword search methods are typically used. However, these are imprecise and unable to handle implicit references or linguistic noise. As an alternative, this study investigates supervised text classification to model and detect suicidality in Dutch-language forum posts. Genetic algorithms were used to optimise models through feature selection and hyperparameter optimisation. A variety of features was found to be informative, including token and character ngram bags-of-words, presence of salient suicide-related terms and features based on LSA topic models and polarity lexicons. The results indicate that text classification is a viable and promising strategy for detecting suicide-related and alarming messages, with F-scores comparable to human annotators (93% for relevant messages, 70% for severe messages). Both types of messages can be detected with high precision and minimal noise, even on large high-skew corpora. This suggests that they would be fit for use in a real-world prevention setting

Multi-class machine classification of suicide-related communication on Twitter

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type

A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy

Text classification, the task of metadata to documents, needs a person to take significant time and effort. Since online-generated contents are explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Recently, various state-or-art text mining methods have been applied to classification process based on the keywords extraction. However, when using these keywords as features in the classification task, it is common that the number of feature dimensions is large. In addition, how to select keywords from documents as features in the classification task is a big challenge. Especially, when using traditional machine learning algorithms in big data, the computation time is very long. On the other hand, about 80% of real data is unstructured and non-labeled in the real world. The conventional supervised feature selection methods cannot be directly used in selecting entities from massive data. Usually, statistical strategies are utilized to extract features from unlabeled data for classification tasks according to their importance scores. We propose a novel method to extract key features effectively before feeding them into the classification assignment. Another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to documents. This problem makes text classification more complicated compared with a single label classification. For the above issues, we develop a framework for extracting data and reducing data dimension to solve the multi-label problem on labeled and unlabeled datasets. In order to reduce data dimension, we develop a hybrid feature selection method that extracts meaningful features according to the importance of each feature. The Word2Vec is applied to represent each document by a feature vector for the document categorization for the big dataset. The unsupervised approach is used to extract features from real online-generated data for text classification. Our unsupervised feature selection method is applied to extract depression symptoms from social media such as Twitter. In the future, these depression symptoms will be used for depression self-screening and diagnosis

Finding the online cry for help : automatic text classification for suicide prevention

Successful prevention of suicide, a serious public health concern worldwide, hinges on the adequate detection of suicide risk. While online platforms are increasingly used for expressing suicidal thoughts, manually monitoring for such signals of distress is practically infeasible, given the information overload suicide prevention workers are confronted with. In this thesis, the automatic detection of suicide-related messages is studied. It presents the first classification-based approach to online suicidality detection, and focuses on Dutch user-generated content. In order to evaluate the viability of such a machine learning approach, we developed a gold standard corpus, consisting of message board and blog posts. These were manually labeled according to a newly developed annotation scheme, grounded in suicide prevention practice. The scheme provides for the annotation of a post's relevance to suicide, and the subject and severity of a suicide threat, if any. This allowed us to derive two tasks: the detection of suicide-related posts, and of severe, high-risk content. In a series of experiments, we sought to determine how well these tasks can be carried out automatically, and which information sources and techniques contribute to classification performance. The experimental results show that both types of messages can be detected with high precision. Therefore, the amount of noise generated by the system is minimal, even on very large datasets, making it usable in a real-world prevention setting. Recall is high for the relevance task, but at around 60%, it is considerably lower for severity. This is mainly attributable to implicit references to suicide, which often go undetected. We found a variety of information sources to be informative for both tasks, including token and character ngram bags-of-words, features based on LSA topic models, polarity lexicons and named entity recognition, and suicide-related terms extracted from a background corpus. To improve classification performance, the models were optimized using feature selection, hyperparameter, or a combination of both. A distributed genetic algorithm approach proved successful in finding good solutions for this complex search problem, and resulted in more robust models. Experiments with cascaded classification of the severity task did not reveal performance benefits over direct classification (in terms of F1-score), but its structure allows the use of slower, memory-based learning algorithms that considerably improved recall. At the end of this thesis, we address a problem typical of user-generated content: noise in the form of misspellings, phonetic transcriptions and other deviations from the linguistic norm. We developed an automatic text normalization system, using a cascaded statistical machine translation approach, and applied it to normalize the data for the suicidality detection tasks. Subsequent experiments revealed that, compared to the original data, normalized data resulted in fewer and more informative features, and improved classification performance. This extrinsic evaluation demonstrates the utility of automatic normalization for suicidality detection, and more generally, text classification on user-generated content

Microevolutionary language theory

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. 219-245).A new microevolutionary theory of complex design within language is proposed. Experiments were carried out that support the theory that complex functional design - adaptive complexity - accumulates due to the evolutionary algorithm at the simplest levels within human natural language. A large software system was developed which identifies and tracks evolutionary dynamics within text discourse. With this system hundreds of examples of activity suggesting evolutionary significance were distilled from a text collection of many millions of words. Research contributions include: (1) An active replicator model of microevolutionary dynamics within natural language, (2) methods to distill active replicators offering evidence of evolutionary processes in action and at multiple linguistic levels (lexical, lexical co-occurrence, lexico-syntactic, and syntactic), (3) a demonstration that language evolution and organic evolution are both examples of a single over-arching evolutionary algorithm, (4) a set of tools to comparatively study language over time, and (5) methods to materially improve text retrieval.by Michael Lloyd Best.Ph.D

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Automatizované metody popisu struktury odborného textu a vztah některých prvků ke kvalitě textu

Universal Semantic Language (USL) is a semi-formalized approach for the description of knowledge (a knowledge representation tool). The idea of USL was introduced by Vladimir Smetacek in the system called SEMAN which was used for keyword extraction tasks in the former Information centre of the Czechoslovak Republic. However due to the dissolution of the centre in early 90's, the system has been lost. This thesis reintroduces the idea of USL in a new context of quantitative content analysis. First we introduce the historical background and the problems of semantics and knowledge representation, semes, semantic fields, semantic primes and universals. The basic methodology of content analysis studies is illustrated on the example of three content analysis tools and we describe the architecture of a new system. The application was built specifically for USL discovery but it can work also in the context of classical content analysis. It contains Natural Language Processing (NLP) components and employs the algorithm for collocation discovery adapted for the case of cooccurences search between semantic annotations. The software is evaluated by comparing its pattern matching mechanism against another existing and established extractor. The semantic translation mechanism is evaluated in the task of...Univerzální sémantický jazyk (USJ) je semi-formalizovaný způsob zápisu znalostí (systém pro reprezentaci znalostí). Myšlenka USJ byla rozvinuta Vladimírem Smetáčkem v 80. letech při pracech na systému SÉMAN (Universální semantický analyzátor). Tento systém byl využíván pro automatizovanou extrakci klíčových slov v tehdejším informačním centru ČSSR. Avšak se zánikem centra v 90. letech byl systém SEMAN ztracen. Tato dizertace oživuje myšlenku USJ v novém kontextu automatizované obsahové analýzy. Nejdříve prezentujeme historický kontext a problémy spojené s reprezentací znalostí, sémů, sémantických polí, sémantických primitivů a univerzálií. Dále je představena metodika kvantitativní obsahové analýzy na příkladu tří klasických aplikací. Podrobně popíšeme architekturu nové aplikace, která byla vyvinuta speciálně pro potřeby evaluace USJ. Program může fungovat jako nástroj pro klasickou obsahovou analýzu, avšak obsahuje i nástroje pro zpracování přirozeného jazyka (NLP) a využívá algoritmů pro vyhledávání kolokací. Tyto byly upraveny pro potřeby vyhledávání vazeb mezi sémantickými anotacemi. Jednotlivé součásti programu jsou podrobeny praktickým testům. Subsystém pro vyhledávní vzorů v textech je porovnán s existujícím extraktorem klíčových slov. Mechanismus pro překlad do sémantických kódů je...Institute of Information Studies and LibrarianshipÚstav informačních studií a knihovnictvíFilozofická fakultaFaculty of Art