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Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning

Sentiment analysis of Facebook statuses using Naive Bayes cl...

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international conference on Information, Intelligence, Systems and Applications

作者： Christos Troussas Maria Virvou Kurt Junshean Espinosa Kevin Llaguno Jaime Caro Department of Informatics University of Piraeus Department of Computer Science University of the Philippines Cebu city

ISBN: (纸本)9781479907694

the growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. the large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. People's feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier.

关键词： Affective interaction Facebook Naive bayes classifier Perceptron classifier Rocchio classifier Sentiment analysis

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Superpixel Clustering and Planar Fit Segmentation of 3D LIDAR Point Clouds

Superpixel Clustering and Planar Fit Segmentation of 3D LIDA...

引用

international conference on Computing for Geospatial Research and Application

作者： Hamid Mahmoudabadi Timothy Shoaf Michael J. Olsen School of Civil and Construction Engineering Oregon State University Department of Electrical Engineering and Computer Science Oregon State University

ISBN: (纸本)9781479903016

Terrestrial laser scanning (TLS, also called ground-based Light Detection and Ranging, LIDAR) is an effective data acquisition method capable of high precision, detailed 3D models for surveying natural environments. However, despite the high density, and quality, of the data itself, the data acquired contains no direct intelligence necessary for further modeling and analysis - merely the 3D geometry (XYZ), 3-component color (RGB), and laser return signal strength (I) for each point. One common task for LIDAR data processing is the selection of an appropriate methodology for the extraction of geometric features from the irregularly distributed point clouds. Such recognition schemes must accomplish both segmentation and classification. Planar (or other geometrically primitive) feature extraction is a common method for point cloud segmentation;however, current algorithms are computationally expensive and often do not utilize color or intensity information. In this paper we present an efficient algorithm, that takes advantage of both colorimetric and geometric data as input and consists of three principal steps to accomplish a more flexible form of feature extraction. First, we employ a Simple Linear Iterative Clustering (SLIC) superpixel algorithm for clustering and dividing the colorimetric data. Second, we use a plane-fitting technique on each significantly smaller cluster to produce a set of normal vectors corresponding to each superpixel. Last, we utilize a Least Squares Multi-class Support Vector machine (LSMSVM) to classify each cluster as either "ground", "wall", or "natural feature". Despite the challenging problems presented by the occlusion of features during data acquisition, our method effectively generates accurate (>85%) segmentation results by utilizing the color space information, in addition to the standard geometry, during segmentation.

关键词： LIDAR Laser Point Cloud Superpixel machine learning Clustering SVM

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E-Book Recommender System Design and Implementation Based on data mining

E-Book Recommender System Design and Implementation Based on...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Wang Zongjiang Weifang Univ Weifang Peoples R China

ISBN: (纸本)9780819490261

In the knowledge explosion, rapid development of information age, how quickly the user or users interested in useful information for feedback to the user problem to be solved in this article. this paper based on data mining, association rules to the model and classification model a combination of electronic books on the recommendation of the user's neighboring users interested in e-books to target users. Introduced the e-book recommendation and the key technologies, system implementation algorithms, and implementation process, was proved through experiments that this system can help users quickly find the required e-books.

关键词： recommendation system e-books data mining association rules

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the application research of Extenics in association rules data mining

The application research of Extenics in association rules da...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Li, Qingshui Jiang, Wenhua Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China Hangzhou Engn Construct Qual & Safety Supervise A Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780819490261

this article describes the theory knowledge of the extenics and association rules, And combined extension and association rule mining algorithm, Construction of a database element model, the complex database reduced to intuitive and simple database, make expression more clear, and reduce the next step rule mining of data calculation. In the basic of Apriori algorithm significant association rules data mining based on the extension. Using association rule mining algorithm and extension of the correlation thought the database of extension of data mining association rules, access to many valuable association rules. And an example illustrates the effectiveness of this method.

关键词： Extenics association rules apriori algorithm extension association rules

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the Study of Personalized Information Recommendation System Based on data mining

The Study of Personalized Information Recommendation System ...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Chen Ke Ke Wende Li Sansi Guangdong Univ Petrochem Technol Dept Comp Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9780819490261

For the current Internet information access of contradictions and difficulties, the study on the basis of the data mining technique and recommender system, propose and implement a facing internet personalization information recommendation system based on data mining. the system is divided into offline and online, offline part to complete the from the site server log files access the appropriate online intelligent personalized recommendation service transaction mode, using the association rules mining. the online part, realizes personalized intelligence recommendation service based on the connection rule excavation. Provides the personalization information referral service method based mining association rules, And through the experiment to this system has carried on the test, has confirmed this system's feasibility and the validity.

关键词： data mining personalized recommendation association rule online part offline part

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A Framework of Chinese Semantic Text mining Based on Ontology learning

A Framework of Chinese Semantic Text Mining Based on Ontolog...

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4th international conference on machine Vision (ICMV) - machine Vision, Image Processing and pattern Analysis

作者： Zhang, Yu-feng Hu, Feng Wuhan Univ Ctr Studies Informat Resources Wuhan Peoples R China Guangdong Univ Technol Sch Management Guangzhou Peoples R China

ISBN: (纸本)9780819490254

Text mining and ontology learning can be effectively employed to acquire the Chinese semantic information. this paper explores a framework of semantic text mining based on ontology learning to find the potential semantic knowledge from the immensity text information on the Internet. this framework consists of four parts: data Acquisition, Feature Extraction, Ontology Construction, and Text Knowledge pattern Discovery. then the framework is applied into an actual case to try to find out the valuable information, and even to assist the consumers with selecting proper products. the results show that this framework is reasonable and effective.

关键词： Text mining semantic mining ontology learning

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Design and Implementation of a Mobile Teaching-learning pattern through Atom Publishing Protocol

Design and Implementation of a Mobile Teaching-Learning Patt...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Wu Yu Tongji Univ Comp Sci & Technol Dept Shanghai 200092 Peoples R China

ISBN: (纸本)9780819490261

the new technologies set the stage for Mobile learning. In this paper, we explored a Mobile Teaching-learning pattern and its advantages. And then we modeled courses with Atom and Atom Publishing Protocol. Grounded on the pattern and modeling, we implemented mobile learning client side with Apple technologies, which could achieve anytime, anywhere learning. And at last, we discussed the application of our system.

关键词： Atompub Mobile Teaching and learning Apple Technology

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A research of E-learning Based On Ontology

A research of E-learning Based On Ontology

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Wang, Zhenxing Shanghai Second Polytech Univ Dept Comp Sci Shanghai Peoples R China

ISBN: (纸本)9780819490261

E-learning device is widely used in our daily life. However mobile e-learning machine is not satisfied as expected. Most mobile e-learning device is a static learning device, unable to fulfill the requirement of collaboration, long standby time, usage under strong sunlight. To meet these requirements, we developed a learning machine based on electronic paper. this paper will discuss the software consideration of the device. the software is a knowledge navigation system, the navigation system is based on Ontology. Research shows that combine frame Ontology and description logic together can afford a uniform interface to user application.

关键词： Ontology embedded system EPD

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A Classification Framework of Online learning Activities: based on Grounded theory

A Classification Framework of Online Learning Activities: ba...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Zhan, Zehui S China Normal Univ Ctr Educ Informat Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9780819490261

the purpose of this paper is to set up a classification framework of online learning activities. Fifty-nine online learning activity cases were collected from seven disciplines. Open coding, axial coding, and selective coding were conducted according to Grounded theory. After step-by-step validation, the classification framework consists of six core categories (Argumentation, Resource Sharing, Collaboration, Inquiry, Evaluation, and Social Network) has been set up. Further study is needed to get more insight into each category and establish effective activity-based instruction models.

关键词： online learning activities classification framework gounded theory

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Application of Complete Local Binary pattern Method for Facial Expression recognition

Application of Complete Local Binary Pattern Method for Faci...

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4th international conference on Intelligent Human Computer Interaction (IHCI)

作者： Singh, Suryakant Maurya, Ritesh Mittal, Ajay GLA Univ Dept Comp Sci & Engn Mathura India PEC Univ Tech Dept Comp Sci Chandigarh India

ISBN: (纸本)9781467343695;9781467343671

We propose a novel approach using Complete Local Binary pattern feature generation method for facial expression recognition with the help of Multi-Class Support Vector machine. Complete Local Binary pattern method is an extended version of Local Binary pattern method with a little difference. LBP feature considers only signs of local differences, whereas CLBP feature considers both signs and magnitude of local differences as well as original center gray level value. CLBP and LBP have same computational complexity while CLBP performs better facial expression recognition over LBP using SVM training and multiclass classification with binary SVM classifiers. the experimental result demonstrate the average efficiency of recognition of propose method (35 images) with CLBP is 86.4%, while with LBP and CCV is 84.1255% and 75.83% in the JAFFE database.

关键词： Color Coherence Vector (CCV) Local Binary pattern (LBP) Completed Local Binary pattern (CLBP) Support Vector machine learning and classification

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