AT-ODTSA: a Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis

In the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets. Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Metaheuristic Clustering Algorithm

In this thesis we describe an essential problem in data clustering and present some solutions for it. We investigate using distance measures other than Euclidean type for improving the performance of clustering. We also develop a new point symmetry-based distance measure and prove its efficiency. We develop a novel effective k-means algorithm which improves the performance of the k-mean algorithm. We develop a dynamic linkage clustering algorithm using kd-tree and we prove its high performance. The Automatic Clustering Differential Evolution (ACDE) is specific to clustering simple data sets and finding the optimal number of clusters automatically. We improve ACDE for classifying more complex data sets using kd-tree. The proposed algorithms do not have a worst-case bound on running time that exists in many similar algorithms in the literature. Experimental results shown in this thesis demonstrate the effectiveness of the proposed algorithms. We compare the proposed algorithms with other famous similar algorithms. We present the proposed algorithms and their performance results in detail along with promising avenues of future research

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

Adaptive Mobility Management Scheme for Mobile IP using Ad Hoc Networks.

Mobile IP has been developed to handle global mobility of mobile hosts. Mobile IP suffers from a number of drawbacks such as frequent location update, high signaling overhead, high handover latency, high packet loss rate, and requirement of infrastructure change. To treat these problems, we propose a new mobility management scheme by constructing dynamic service regions of Ad Hoc networks for roaming mobile host without location update to reduce signaling overhead and packets loss. Analytical analysis and simulation results are shown in this paper to demonstrate that the proposed scheme performs better performance than other schemes

Clasificación de sentimientos semi-supervisada y dependiente de objetivo para micro- blogs

The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semisupervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.Facultad de Informátic

K-means algorithm with a novel distance measure

In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also developed an improved point symmetry-based distance measure and proved its efficiency. We developed a k-means algorithm with a novel distance measure that improves the performance of the classical k-means algorithm. The proposed algorithm does not have the worst-case bound on running time that exists in many similar algorithms in the literature. Experimental results shown in this paper demonstrate the effectiveness of the proposed algorithm. We compared the proposed algorithm with the classical k-means algorithm. We presented the proposed algorithm and their performance results in detail along with avenues of future research

Hybrid deep-readout echo state network and support vector machine with feature selection for human activity recognition

Developing sophisticated automated systems for assisting numerous humans such as patients and elder people is a promising future direction. Such smart systems are based on recognizing Activities of Daily Living (ADLs) for providing a suitable decision. Activity recognition systems are currently employed in developing many smart technologies (e.g., smart mobile phone) and their uses have been increased dramatically with availability of Internet of Things (IoT) technology. Numerous machine learning techniques are presented in literature for improving performance of activity recognition. Whereas, some techniques have not been sufficiently exploited with this research direction. In this paper, we shed the light on this issue by presenting a technique based on employing Echo State Network (ESN) for human activity recognition. The presented technique is based on combining ESN with Support Vector Machine (SVM) for improving performance of activity recognition. We also applied feature selection method to the collected data to decrease time complexity and increase the performance. Many experiments are conducted in this work to evaluate performance of the presented technique with human activity recognition. Experiment results have shown that the presented technique provides remarkable performance

A dynamic linkage clustering using KD-tree.

Some clustering algorithms calculate connectivity of each data point to its cluster by depending on density reachability. These algorithms can find arbitrarily shaped clusters, but they require parameters that are mostly sensitive to clustering performance. We develop a new dynamic linkage clustering algorithm using kd-tree. The proposed algorithm does not require any parameters and does not have a worst-case bound on running time that exists in many similar algorithms in the literature. Experimental results are shown in this paper to demonstrate the effectiveness of the proposed algorithm. We compare the proposed algorithm with other famous similar algorithm that is shown in literature. We present the proposed algorithm and its performance in detail along with promising avenues of future research