Sentiment Classification Using Supervised and Unsupervised Approach

In past few years, the data available on internet has multiplied at an alarming rate. Tweets, reviews, blogs and comments on social media have been a huge factor which has resulted in such a huge amount of increase in the available data. Because of this datasets being highly unstructured and of high dimensionality, sentiment classification becomes a very tiresome task. Sentiment Analysis is used to estimate the user opinion on various issues. It consequently mines states of mind and perspectives of clients on particular issues. It�s a multistep preparation where choosing and extracting elements is an indispensable stride that controls execution of sentiment classifier. In this paper we have used three supervised techniques namely SVM, Decision Tree and Nave Bays Algorithm and three unsupervised techniques called DE, PSO and K-Means The results are validated using different three benchmark labeled datasets data sets and on the different feature sets We have also performed feature selection using genetic algorithm and validated results using the features selected by the GA Experimental results shows that supervised techniques have outperformed supervised techniques on one dataset while for the two datasets supervised techniques have outperformed unsupervised technique

Multi-class SVMs: A Relative Study of Kernels

Support Vector Machine is a powerful classification technique based on the idea of Structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and till now there is no good method on how to choose a kernel function. In this paper, the choice of the kernel function was studied empirically and optimal results were achieved for multi-class SVM by combining several binary classifiers. The performance of the multi-class SVM is illustrated by extensive experimental results which indicate that with suitable kernel and parameters better classification accuracy can be achieved as compared to other methods. The experimental results of three datasets show that Gaussian kernel is not always the best choice to achieve high generalization of classifier although it often the default choice

Sine Cosine Based Algorithm for Data Clustering

K-Means clustering algorithm is simple and prevalent, but it has a basic problem to stuck at local optima which relies on randomly generated centroid positions. Optimization algorithms are outstanding for their capacity to lead iterative computation in looking for global optima. Clustering analysis, in today�s world, is an important tool and seeking to recognize homogeneous groups of objects on the basis of values of attributes in many fields and applications. In this paper we have proposed a Sine Cosine based algorithm for data clustering (SCBAFDC). The proposed algorithm is tested on five benchmark datasets and compared with other five clustering algorithms. The results show that the proposed algorithm is giving competitive results as compared to the other algorithms in terms of quality of clustering

Optimal Hierarchical Structure Design of Decision Tree SVM Using Distance Based Approach

In literature multi-class SVM is constructed using One against All, One against One and Decision tree based SVM using Euclidean and Mahalanobis distance. To maintain high generalization ability, the most separable classes should be separated at the upper nodes of decision tree. In this paper, A deterministic quantitative model based on distance based approach (DBA) method has been developed and applied for evaluation, optimal selection SVM model for the first time. DBA recognizes the need for relative importance of criteria for a given application, without which inter-criterion comparison could not be accomplished. It requires a set of model selection criteria like information gain, gini index, chi-squared, chernoff-bound, kullbak divergence and scatter-matrix-based class separability in kernel-induced space, along with a set of SVM Models and their level of criteria for optimal selection, and successfully presents the results in terms of a merit value which is used to rank the SVM models. One real dataset from distinct published papers have been used for demonstration of DBA method. The result of this study will be a selection of SVM Model at the root node of decision tree One Versus One (OvO) SVM based on the Euclidean composite distance of each alternative to the designated optimal SVM Model. It is shown that the Optimal Decision Tree (ODT) SVM requires less computation time in comparison to conventional One against All SVM. Experimental results on UCI repository dataset demonstrates better or equivalent performance of our proposed decision tree scheme in comparison to conventional One versus One (OvO) SVM in terms of classification accuracy for most of the datasets. The proposed scheme outperforms conventional One versus One SVM in terms of computation time for both training and testing phase using DBA approach employed for determining the structure of decision tree

Experiential Study of Kernel Functions to Design an Optimized Multi-class SVM

Support Vector Machine is a powerful classification technique based on the idea of Structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and till now there is no good method on how to choose a kernel function. In this paper, the choice of the kernel function was studied empirically and optimal results were achieved for multi-class SVM by combining several binary classifiers. The performance of the multi-class SVM is illustrated by extensive experimental results which indicate that with suitable kernel and parameters better classification accuracy can be achieved as compared to other methods. The experimental results of three datasets show that Gaussian kernel is not always the best choice to achieve high generalization of classifier although it often the default choice

Statistical Measures to Determine Optimal Structure of Decision Tree: One versus One Support Vector Machine

In this paper, one versus one optimal decision tree support vector machine (OvO-ODT SVM) framework is proposed to solve multi-class problems where the optimal structure of decision tree is determined using statistical measures, i.e., information gain, gini index, and chi-square. The performance of proposed OvO-ODT SVM is evaluated in terms of classification accuracy and computation time. It is also shown that proposed OvO-ODT SVM using all the three measures is more efficient in terms of time complexity for both training and testing phases in comparison to conventional OvO and support vector machine binary decision tree (SVMBDT). Experiments on University of California, Irvine (UCI) repository dataset illustrates that ten crossvalidation accuracy of our proposed framework is comparable or better in comparison to conventional OvO and SVM-BDT for most of the datasets. However, the proposed framework outperforms the conventional OvO and SVM-BDT for all the datasets in terms of both training and testing time.Defence Science Journal, 2010, 60(4), pp.399-404, DOI:http://dx.doi.org/10.14429/dsj.60.50

Spatiotemporal Saliency Detection: State of Art

Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made

Text Recognition Past, Present and Future

Text recognition in various images is a research domain which attempts to develop a computer programs with a feature to read the text from images by the computer. Thus there is a need of character recognition mechanisms which results Document Image Analysis (DIA) which changes different documents in paper format computer generated electronic format. In this paper we have read and analyzed various methods for text recognition from different types of text images like scene images, text images, born digital images and text from videos. Text Recognition is an easy task for people who can read, but to make a computer that does character recognition is highly difficult task. The reasons behind this might be variability, abstraction and absence of various hard-and-fast rules that locate the appearance of a visual character in various text images. Therefore rules that is to be applied need to be very heuristically deduced from samples domain. This paper gives a review for various existing methods. The objective of this paper is to give a summary on well-known methods