Roliana Ibrahim

A systematic literature review of software requirements prioritization research

Context: During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases. It is a major step taken in making crucial decisions so as to increase the economic value of a system. Objective: The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions. Method: Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins. Results: 73 Primary studies were selected from the search processes. Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers. Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13. Conclusion: Prioritization has been significantly discussed in the requirements engineering domain. However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues. Also, the applicability of existing techniques in complex and real setting has not been reported yet.

Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples

We combine active learning and self-training for cross-lingual sentiment classification.Density analysis of unlabelled data is used to select representative examples in active learning.We test our proposed model on three different target languages.Results show that incorporating density analysis can speed up learning process.Results show that combination of two approaches outperforms each individual method. In recent years, research in sentiment classification has received considerable attention by natural language processing researchers. Annotated sentiment corpora are the most important resources used in sentiment classification. However, since most recent research works in this field have focused on the English language, there are accordingly not enough annotated sentiment resources in other languages. Manual construction of reliable annotated sentiment corpora for a new language is a labour-intensive and time-consuming task. Projection of sentiment corpus from one language into another language is a natural solution used in cross-lingual sentiment classification. Automatic machine translation services are the most commonly tools used to directly project information from one language into another. However, since term distribution across languages may be different due to variations in linguistic terms and writing styles, cross-lingual methods cannot reach the performance of monolingual methods. In this paper, a novel learning model is proposed based on the combination of uncertainty-based active learning and semi-supervised self-training approaches to incorporate unlabelled sentiment documents from the target language in order to improve the performance of cross-lingual methods. Further, in this model, the density measures of unlabelled examples are considered in active learning part in order to avoid outlier selection. The empirical evaluation on book review datasets in three different languages shows that the proposed model can significantly improve the performance of cross-lingual sentiment classification in comparison with other existing and baseline methods.

Bi-view semi-supervised active learning for cross-lingual sentiment classification

Recently, sentiment classification has received considerable attention within the natural language processing research community. However, since most recent works regarding sentiment classification have been done in the English language, there are accordingly not enough sentiment resources in other languages. Manual construction of reliable sentiment resources is a very difficult and time-consuming task. Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, different term distribution between original and translated text documents and translation errors are two main problems faced in the case of using only machine translation. To overcome these problems, we propose a novel learning model based on active learning and semi-supervised co-training to incorporate unlabelled data from the target language into the learning process in a bi-view framework. This model attempts to enrich training data by adding the most confident automatically-labelled examples, as well as a few of the most informative manually-labelled examples from unlabelled data in an iterative process. Further, in this model, we consider the density of unlabelled data so as to select more representative unlabelled examples in order to avoid outlier selection in active learning. The proposed model was applied to book review datasets in three different languages. Experiments showed that our model can effectively improve the cross-lingual sentiment classification performance and reduce labelling efforts in comparison with some baseline methods.

Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook)

This research examines the relationship between the predictors of use and the different conceptualizations of system use in a hedonic volitional setting (Facebook). Using the unified theory of acceptance and use of technology (UTAUT) model, an investigation into the three aspects of system use: the user, system and task were carried out. Results from a cross-sectional survey of 449 students show that behavioral intention has a significant influence on all aspects and dimensions of system use including cognitive absorption and deep structure use. Performance expectancy, effort expectancy and social influence are significantly related to system use. From the component model, performance expectancy is only significant with deep structure use. Hedonic performance expectancy is found to be significantly related to cognitive absorption. Results also demonstrate that predictors of usage have a significant relationship with the user aspect of system use. The variance explained in usage conceptualized as the user/task aspects is much higher than that of the system/task aspects or one-dimensional measures. Overall, conceptualizing system use using the user/task aspects offers greater explanatory power in Facebook use.

Hybridized term-weighting method for Dark Web classification

The role of intelligence and security informatics based on statistical computations is becoming more significant in detecting terrorism activities proactively as the extremist groups are misusing many of the obtainable facilities on the Internet to incite violence and hatred. However, the performance of statistical methods is limited due to the inadequate accuracy produced by the inability of these methods to comprehend the texts created by humans. In this paper, we propose a hybridized feature selection method based on the basic term-weighting techniques for accurate terrorism activities detection in textual contexts. The proposed method combines the feature sets selected based on different individual feature selection methods into one feature space for effective web pages classification. UNION and Symmetric Difference combination functions are proposed for dimensionality reduction of the combined feature space. The method is tested on a selected dataset from the Dark Web Forum Portal and benchmarked using various famous text classifiers. Experimental results show that the hybridized method efficiently identifies the terrorist activities content and outperforms the individual methods. Furthermore, the results revealed that the classification performance achieved by hybridizing few feature sets is relatively competitive in the number of features used for classification with higher hybridization levels. Moreover, the experiments of hybridizing functions show that the dimensionality of the feature sets is significantly reduced by applying the Symmetric Difference function for feature sets combination. A hybrid text classifications method with term-weighting techniques is proposed.The proposed method combines various feature sets for effective classification.We proposed a method to reduce the dimension of feature sets for classification.The method is tested on a selected dataset from the Dark Web Portal Forum.Experimental results show that the proposed method outperforms other methods.

Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning

Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, due to the existence of differing linguistic terms and writing styles between different languages, translated data cannot cover all vocabularies which exist in the original data. Further, different term distribution between translated data and original data can lead to low performance in cross-lingual sentiment classification. To overcome these problems, we propose a new model which uses labelled data from multiple source languages in a multi-view semi-supervised learning approach so as to incorporate unlabelled data from the target language into the learning process. The proposed model was applied to book review datasets in four different languages. Experiments have shown that our model can effectively improve the cross-lingual sentiment classification performance in comparison with some baseline methods.

Towards Data Quality into the Data Warehouse Development

Commonly, DW development methodologies, paying little attention to the problem of data quality and completeness. One of the common mistakes made during the planning of a data warehousing project is to assume that data quality will be addressed during testing. In addition to the data warehouse development methodologies, we will introduce in this paper a new approach to data warehouse development. This proposal will be based on integration data quality into the whole data warehouse development phase, denoted by: integrated requirement analysis for designing data warehouse (IRADAH). This paper shows that data quality is not only an integrated part of data warehouse project, but will remain a sustained and ongoing activity.

Hierarchical cluster ensemble selection

Abstract Clustering ensemble performance is affected by two main factors: diversity and quality. Selection of a subset of available ensemble members based on diversity and quality often leads to a more accurate ensemble solution. However, there is not a certain relationship between diversity and quality in selection of subset of ensemble members. This paper proposes the Hierarchical Cluster Ensemble Selection (HCES) method and diversity measure to explore how diversity and quality affect final results. The HCES uses single-link, average-link, and complete link agglomerative clustering methods for the selection of ensemble members hierarchically. A pair-wise diversity measure from the recent literature and the proposed diversity measure are applied to these agglomerative clustering algorithms. Using the proposed diversity measure in HCES leads to more diverse ensemble members than that of pairwise diversity measure. Cluster-based Similarity Partition Algorithm (CSPA) and Hypergraph-Partitioning Algorithm (HGPA) were employed in HCES method for obtaining the full ensemble and cluster ensemble selection solution. To evaluate the performance of the HCES method, several experiments were conducted on several real data sets and the obtained results were compared to those of full ensembles. The results showed that the HCES method led to a more significant performance improvement compared with full ensembles.

Ordinal-based and frequency-based integration of feature selection methods for sentiment analysis

Feature subset selection with the aim of reducing dependency of feature selection techniques and obtaining a high-quality minimal feature subset from a real-world domain is the main task of this research. For this end, firstly, two types of feature representation are presented for feature sets, namely unigram-based and part-of-speech based feature sets. Secondly, five methods of feature ranking are employed for creating feature vectors. Finally, we propose two methods for the integration feature vectors and feature subsets. An ordinal-based integration of different feature vectors (OIFV) is proposed in order to obtain a new feature vector. The new feature vector depends on the order of features in the old vectors. A frequency-based integration of different feature subsets (FIFS) with most effective features, which are obtained from a hybrid filter and wrapper methods in the feature selection task, is then proposed. In addition, four well-known text classification algorithms are employed as classifiers in the wrapper method for the selection of the feature subsets. A wide range of comparative experiments on five widely-used datasets in sentiment analysis were carried out. The experiments demonstrate that proposed methods can effectively improve the performance of sentiment classification. These results also show that proposed part-of-speech patterns are more effective in their classification accuracy compared to unigram-based features.

Density based active self-training for cross-lingual sentiment classification

Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, since machine translation quality is still far from satisfactory and also term distribution across languages may be dissimilar, these techniques cannot reach the performance of monolingual approaches. To overcome these limitations, we propose a novel learning model based on active learning and self-training to incorporate unlabeled data from the target language into the learning process. Further, in this model, we consider the density of unlabeled data to avoid outlier selection in active learning. The proposed model was applied to book review datasets in two different languages. Experiments showed that the proposed model could effectively reduce labeling efforts in comparison with some baseline methods.