The Study of Man. By Michael Polanyi. Price, $1.75. Pp. 102. University of Chicago Press, 5750 Ellis Ave., Chicago 37, 1959. One subtitle to Polanyi's challenging and fascinating book might be The Evolution and Natural History of Error , for Polanyi is at pains to expunge what he believes to be the false notion contained in the contemporary view of science which treats it as an object and basically impersonal discipline. According to Polanyi not only is this a radical and important error, but it is harmful to the objectives of science itself. Another subtitle could be Farewell to Detachment , for in place of cold objectivity he develops the idea that science is necessarily intensely personal. It is a human endeavor and human point of view which cannot be divorced from nor uprooted out of the human matrix from which it arises and in which it works. For a good while

Personal Knowledge: Towards a Post-Critical Philosophy

Knowledge Management

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

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A Survey on Evolutionary Computation Approaches to Feature Selection

Ensemble methods use multiple models to get better performance. Ensemble methods have been used in multiple research fields such as computational intelligence, statistics and machine learning. This paper reviews traditional as well as state-of-the-art ensemble methods and thus can serve as an extensive summary for practitioners and beginners. The ensemble methods are categorized into conventional ensemble methods such as bagging, boosting and random forest, decomposition methods, negative correlation learning methods, multi-objective optimization based ensemble methods, fuzzy ensemble methods, multiple kernel learning ensemble methods and deep learning based ensemble methods. Variations, improvements and typical applications are discussed. Finally this paper gives some recommendations for future research directions.

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

Computers and the collaborative experience of learning

Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

Aspect extraction in sentiment analysis: comparative analysis and survey

Opinion target extraction or aspect extraction is the most important subtask of the aspect-based sentiment analysis. This task focuses on the identification of the targets of user's opinions or sentiments from online reviews. In the recent years, syntactic patterns-based approaches have performed quite well and produced significant improvement in the aspect extraction task. However, these approaches are heavily dependent on the dependency parsers which produced syntactic relations following the grammatical rules and language constraints. In contemporary, users do not give much importance to these rules and constraints while expressing their opinions about particular product and neither reviewer websites restrict users to do so. This makes syntactic patterns-based approaches vulnerable. Therefore, in this paper, we are proposing a two-fold rules-based model (TF-RBM) which uses rules defined on the basis of sequential patterns mined from customer reviews. The first fold extracts aspects associated with domain independent opinions and the second fold extracts aspects associated with domain dependent opinions. We have also applied frequency- and similarity-based approaches to improve the aspect extraction accuracy of the proposed model. Our experimental evaluation has shown better results as compared with the state-of-the-art and most recent approaches.

A two-fold rule-based model for aspect extraction

Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism-i.e., clinical scenarios-that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.

A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models

With the expansion and acceptance of Word Wide Web, sentiment analysis has become progressively popular research area in information retrieval and web data analysis. Due to the huge amount of user-generated contents over blogs, forums, social media, etc., sentiment analysis has attracted researchers both in academia and industry, since it deals with the extraction of opinions and sentiments. In this paper, we have presented a review of topic modeling, especially LDA-based techniques, in sentiment analysis. We have presented a detailed analysis of diverse approaches and techniques, and compared the accuracy of different systems among them. The results of different approaches have been summarized, analyzed and presented in a sophisticated fashion. This is the really effort to explore different topic modeling techniques in the capacity of sentiment analysis and imparting a comprehensive comparison among them.

Yu-N Cheah

Papers

Aspect extraction in sentiment analysis: comparative analysis and survey

A two-fold rule-based model for aspect extraction

A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models

Topic Modeling in Sentiment Analysis: A Systematic Review