2016 7th International Conference on Computer Science and Information Technology (CSIT)最新文献

An ontology for Juz' Amma based on expert knowledge 基于专家知识的Juz' Amma本体

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-08-25 DOI: 10.1109/CSIT.2016.7549480

Noor Siti Husnah Ab Rahim Periamalai, A. Mustapha, Ahmad Alqurneh

引用次数: 5

Arabic OCR evaluation tool 阿拉伯语OCR评价工具

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549460

Mansoor Alghamdi, Ibrahim Alkhazi, W. Teahan

引用次数: 15

Evaluating SentiStrength for Arabic Sentiment Analysis 评估阿拉伯语情感分析的SentiStrength

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549458

Abdullateef Rabab'ah, M. Al-Ayyoub, Y. Jararweh, M. Al-Kabi

{"title":"Evaluating SentiStrength for Arabic Sentiment Analysis","authors":"Abdullateef Rabab'ah, M. Al-Ayyoub, Y. Jararweh, M. Al-Kabi","doi":"10.1109/CSIT.2016.7549458","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549458","url":null,"abstract":"Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed for English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful for English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Towards data driven decision support for financial institutions: Predicting small companies business volume in Switzerland 面向金融机构的数据驱动决策支持:预测瑞士小公司的业务量

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549449

Daniel Müller, Funk Te, Flavien Meyer, Irena Pletikosa Cvijikj

引用次数: 2

Empirical insight into the context of design patterns: Modularity analysis 对设计模式上下文的经验洞察:模块化分析

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549474

Mawal A. Mohammed, Mahmoud O. Elish, A. Qusef

引用次数: 5

Social Media in project communications management 项目沟通管理中的社交媒体

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549448

A. Qusef, Khaled Ismail

引用次数: 7

Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering 基于遗传算法的无监督特征选择技术改进文本聚类

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549453

L. Abualigah, A. Khader, M. Al-Betar

{"title":"Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering","authors":"L. Abualigah, A. Khader, M. Al-Betar","doi":"10.1109/CSIT.2016.7549453","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549453","url":null,"abstract":"The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised learning technique. This technique is used to select informative features to improve the performance of text clustering algorithm. Recently, the meta-heuristic algorithms are successfully applied to solve several hard optimization problems. In this paper, we proposed the genetic algorithm (GA) to solve the unsupervised feature selection problem, namely, (FSGATC). This method is used to create a new subset of informative features in order to obtain more accurate clusters. Experiments were conducted using four benchmark text datasets with variant characteristics. The results showed that the proposed FSGATC is improved the performance of the text clustering algorithm and got better results compared with k-mean clustering standalone. Finally, the proposed method “FSGATC” evaluated by F-measure and Accuracy, which are common measures used in the domain of text clustering.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 55

Are emoticons good enough to train emotion classifiers of Arabic tweets? 表情符号是否足以训练阿拉伯语推文的情感分类器?

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549459

Wegdan A. Hussien, Yahya M. Tashtoush, M. Al-Ayyoub, M. Al-Kabi

{"title":"Are emoticons good enough to train emotion classifiers of Arabic tweets?","authors":"Wegdan A. Hussien, Yahya M. Tashtoush, M. Al-Ayyoub, M. Al-Kabi","doi":"10.1109/CSIT.2016.7549459","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549459","url":null,"abstract":"Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 48

Unsupervised feature selection technique based on harmony search algorithm for improving the text clustering 基于和谐搜索算法的无监督特征选择技术改进文本聚类

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549456

L. Abualigah, A. Khader, M. Al-Betar

引用次数: 17

Multi-objectives-based text clustering technique using K-mean algorithm 基于k -均值算法的多目标文本聚类技术

2016 7th International Conference on Computer Science and Information Technology (CSIT) Pub Date : 2016-07-13 DOI: 10.1109/CSIT.2016.7549464

L. Abualigah, A. Khader, M. Al-Betar

{"title":"Multi-objectives-based text clustering technique using K-mean algorithm","authors":"L. Abualigah, A. Khader, M. Al-Betar","doi":"10.1109/CSIT.2016.7549464","DOIUrl":"https://doi.org/10.1109/CSIT.2016.7549464","url":null,"abstract":"Text documents clustering is a popular unsupervised text mining tool. It is used for partitioning a collection of text documents into similar clusters based on the distance or similarity measure as decided by an objective function. Text clustering algorithm often makes prior assumptions to satisfy objective function, which is optimized either through traditional techniques or meta-heuristic techniques. In text clustering techniques, the right decision for any document distribution is done using an objective function. Normally, clustering algorithms perform poorly when the configuration of the well-formulated objective function is not sound and complete. Therefore, we proposed multi-objectives-based method namely, combine distance and similarity measure for improving the text clustering technique. Multi-objectives text clustering method is combined with two evaluating criteria which emerge as a robust alternative in several situations. In particular, the multi-objective function in the text clustering domain is not a popular, and it is a core issue that affects the performance of the text clustering technique. The performance of multi-objectives function is investigated using the k-mean text clustering technique. The experiments were conducted using seven standard text datasets. The results showed that the proposed multi-objectives based method outperforms the other measures in term of the performance of the text clustering, evaluated by using two common clustering measures, namely, Accuracy and F-measure.","PeriodicalId":210905,"journal":{"name":"2016 7th International Conference on Computer Science and Information Technology (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43