IJARAI Volume 3 Issue 11

Paper 1: Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles

Abstract: Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including Sentiment Analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu. While dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. In this paper, our main focus was on evaluating and comparing three sentiment analysis methods (two machine learning based and one lexical based) and also identifying the scope of negation in news articles for two political parties namely BJP and UPA by using three existing methodologies. They were Rest of the Sentence (RoS), Fixed Window Length (FWL) and Dependency Analysis (DA). Among the sentiment methods the best F-measure was SVM with the values 0.688 and 0.657 for BJP and UPA respectively. On the other hand, the F measures for RoS, FWL and DA were 0.58, 0.69 and 0.75 respectively. We observed that DA was performing better than the other two. Among 1675 sentences in the corpus, according to annotator I, 1,137 were positive and 538 were negative whereas according to annotator II, 1,130 were positive and 545 were negative. Further we also identified the score of each sentence and calculated the accuracy on the basis of average score of both the annotators.

Author 1: S Padmaja

Author 2: Prof. S Sameen Fatima

Author 3: Sasidhar Bandu

Keywords: Sentiment Analysis; Negation Identification; News Articles

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Paper 2: An Information-Theoretic Measure for Face Recognition: Comparison with Structural Similarity

Abstract: Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM).

Author 1: Asmhan Flieh Hassan

Author 2: Zahir M. Hussain

Author 3: Dong Cai-lin

Keywords: Information Theoretic Similarity; Joint Histogram; Structural Similarity (SSIM); face recognition; Image Processing

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Paper 3: A Multistage Feature Selection Model for Document Classification Using Information Gain and Rough Set

Abstract: Huge number of documents are increasing rapidly, therefore, to organize it in digitized form text categorization becomes an challenging issue. A major issue for text categorization is its large number of features. Most of the features are noisy, irrelevant and redundant, which may mislead the classifier. Hence, it is most important to reduce dimensionality of data to get smaller subset and provide the most gain in information. Feature selection techniques reduce the dimensionality of feature space. It also improves the overall accuracy and performance. Hence, to overcome the issues of text categorization feature selection is considered as an efficient technique . Therefore, we, proposed a multistage feature selection model to improve the overall accuracy and performance of classification. In the first stage document preprocessing part is performed. Secondly, each term within the documents are ranked according to their importance for classification using the information gain. Thirdly rough set technique is applied to the terms which are ranked importantly and feature reduction is carried out. Finally a document classification is performed on the core features using Naive Bayes and KNN classifier. Experiments are carried out on three UCI datasets, Reuters 21578, Classic 04 and Newsgroup 20. Results show the better accuracy and performance of the proposed model.

Author 1: Mrs. Leena. H. Patil

Author 2: Dr. Mohammed Atique

Keywords: Introduction; Document Preprocessing; Information Gain; Rough Set; Classifiers

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Paper 4: Double Competition for Information-Theoretic SOM

Abstract: In this paper, we propose a new type of informationtheoretic method for the self-organizing maps (SOM), taking into account competition between competitive (output) neurons as well as input neurons. The method is called ”double competition”, as it considers competition between outputs as well as input neurons. By increasing information in input neurons, we expect to obtain more detailed information on input patterns through the information-theoretic method. We applied the informationtheoretic methods to two well-known data sets from the machine learning database, namely, the glass and dermatology data sets. We found that the information-theoretic method with double competition explicitly separated the different classes. On the other hand, without considering input neurons, class boundaries could not be explicitly identified. In addition, without considering input neurons, quantization and topographic errors were inversely related. This means that when the quantization errors decreased, topographic errors inversely increased. However, with double competition, this inverse relation between quantization and topographic errors was neutralized. Experimental results show that by incorporating information in input neurons, class structure could be clearly identified without degrading the map quality to severely.

Author 1: Ryotaro Kamimura

Keywords: double competition, self-organizing maps, mutual information, class structure

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Paper 5: Mobile Learning-system usage: Scale development and empirical tests

Abstract: Mobile technologies have changed the shape of learning for learners, society, and education providers. Consequently, mobile learning has become a core component in modern education. Nevertheless, introducing mobile learning systems does not automatically guarantee that learners will develop a positive behavioural intention to use it and therefore use it. Thus, acceptance-of-technology and system-success studies have increased. As yet, however, much of the research regarding understanding students’ behavioural intention to use mobile learning systems seems to suffer from several shortcomings. On top of that, there is no common cognitive theoretical foundation. This study introduces a theoretical framework that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) and Information System (IS) Success Model. This integration resulted in three success measures and two acceptance constructs. The success measures included the following: a) information quality, b) system quality, and c) user satisfaction; whilst the following were the acceptance measures: a) effort expectancy, b) performance expectancy, and c) social influence. Further, this study introduces lecture attitude as a new construct that is believed to moderate students’ behavioural intention. The relationships between the different factors form the research hypotheses.

Author 1: Saleh Alharbi

Author 2: Steve Drew

Keywords: Mobile learning; Mobile learning; Higher education; UTAUT; IS Success

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Paper 6: Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing

Abstract: The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.

Author 1: Hasibur Rahman

Author 2: Rahim Rahmani

Author 3: Theo Kanter

Keywords: Internet; crowdsourcing; pervasive computing; context information; dynamism; context-ID; logical-clustering; Publish/Subscribe; MediaSense

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