DSpace Repository :: Browsing by Author "Naomie Salim"

Browsing by Author "Naomie Salim"

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A correlation analysis between sentimental comment and numerical response in students' feedback
(Asian Research Publishing Network, 2015) Phuripoj Kaewyong; Anupong Sukprasert; Naomie Salim; Fatin Aliah Phang; P. Kaewyong; Information Technology Department, Suan Dusit University, Thailand; email: phuripoj@yahoo.com
This paper aims to study a qualitative measuring of students' comments using sentiment analysis to teacher evaluation and investigate its qualitative analysis. A small dataset of students' feedbacks was collected from the public website and was utilized in the experimental. We performed the lexicon based sentiment analysis to identify sentiment word and determine overall sentiment polarity of students' comment into positive and negative classes based on Opinion Lexicon automatically. A comparison between overall sentiment scores and numerical response scores of teacher evaluation aspects were evaluated and plotted into graphs in order to compare the relationship between each pair of two variables. Especially, we applied the statistical techniques using Pearson's correlation and Spearman's rank to confirm these visual correlation results. The experimental results suggested that there is a significant correlation between overall sentiment scores from its qualitative analysis and numerical response scores of teacher evaluation aspects. Based on this, it might be possible to convert from qualitative to quantitative type of teacher evaluation by performing lexicon based sentiment analysis.
Feature-based sentence extraction using fuzzy inference rules
(2009) Ladda Suanmali; Naomie Salim; Mohammed Salem Binwahlan; L. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.th
Automatic text summarization is a wide research area. Automatic text summarization is to compress the original text into a shorter version and help the user to quickly understand large volumes of information. There are several ways in which one can characterize different approaches to text summarization: extractive and abstractive from single document or multi document. This paper focuses on the automatic text summarization by sentence extraction. The first step in summarization by extraction is the identification of important features. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 8 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries are conducted from fuzzy method. © 2009 IEEE.
Fuzzy genetic semantic based text summarization
(2011) Ladda Suanmali; Naomie Salim; Mohammed Salem Binwahlan; L. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.th
Automatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sentence based on a sentence scoring approach. Highly ranked sentences are used for the summary. This paper proposed an automatic text summarization approach based on sentence extraction using fuzzy logic, genetic algorithm, semantic role labeling and their combinations to generate high quality summaries. This study explored the benefits of the genetic algorithm in the optimization problem in for feature selection during the training phase and adjusts feature weights during the testing phase. Fuzzy IF-THEN rules were used to balance the weights between important and unimportant features. Conventional extraction methods cannot capture semantic relations between concepts in a text. Therefore, this research investigates the use of the semantic role labeling to capture the semantic contents in sentences and incorporate it into the summarization method. This paper is evaluated in terms of performance using ROUGE toolkit. Experimental results showed that the summaries produced by the proposed approaches are better than other approaches produced by Microsoft Word 2007, Copernic Summarizer, and MANYASPECTS summarizers. © 2011 IEEE.
Fuzzy swarm based text summarization
(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali
Problem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not. © 2009 Science Publications.
Fuzzy swarm diversity hybrid model for text summarization
(2010) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali; M. S. Binwahlan; Faculty of Applied Sciences, Hadhramout University of Science and Technology, Yemen, 81310 Skudai, Johor, Malaysia; email: moham2007med@yahoo.com
High quality summary is the target and challenge for any automatic text summarization. In this paper, we introduce a different hybrid model for automatic text summarization problem. We exploit strengths of different techniques in building our model: we use diversity-based method to filter similar sentences and select the most diverse ones, differentiate between the more important and less important features using the swarm-based method and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text features weights flexibly tolerated. The diversity-based method focuses to reduce redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We presented the proposed model in two forms. In the first form of the model, diversity measures dominate the behavior of the model. In the second form, the diversity constraint is no longer imposed on the model behavior. That means the diversity-based method works same as fuzzy swarm-based method. The results showed that the proposed model in the second form performs better than the first form, the swarm model, the fuzzy swarm method and the benchmark methods. Over results show that combination of diversity measures, swarm techniques and fuzzy logic can generate good summary containing the most important parts in the document. © 2010 Elsevier Ltd. All rights reserved.
Independent component analysis and rough fuzzy based approach to web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salim Bin Ngadiman; Witcha Chimphlee; Surat Srinoy; S. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siripom.chi@dusit.ac.th
Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. It aims at discovery of trends and regularities in web users' access patterns. ICA is a very general-purpose statistical technique in which observed random data are linearly transformed into components that are maximally independent from each other, and simultaneously have "interesting" distributions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/impreciseness accuracy.
Pseudo genetic and probabilistic-based feature selection method for extractive single document summarization
(Asian Research Publishing Network (ARPN), 2011) Albaraa Abuobieda M. ALI; Naomie Salim; Rihab Eltayeb Ahmed; Mohammed Salem Binwahlan; Ladda Suanmali; Ahmed Hamza; A. A. M. ALI; Faculty of Computer Science and Information Systems, University Technology Malaysia, 81310, Johor, Malaysia; email: albarraa@hotmail.com
Text features, as a scoring mechanism, are used to identify the key ideas in a given document to be represented in the text summary. Considering all features within same the level of importance may lead to generate a summary with low quality. In this paper, we present a feature selection method using (pseudo) Genetic probabilistic-based Summarization (PGPSum) model for extractive single document summarization. The proposed method, working as features selection mechanism, is used to extract the weights of features from texts. Then, the weights will be used to tune features' scores in order to optimize the summarization process. In this way, important sentences will be selected for representing the document summary. The results show that, our PGPSum model outperformed Ms-Word and Copernic summarizers benchmarks by obtaining a similarity ratio closest to human benchmark summary. © 2005 - 2011 JATIT & LLS. All rights reserved.
Rough fuzzy approach for web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha Chimphlee; Surat Srinoy; S. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Rd, Thailand; email: siriporn_chi@dusit.ac.th
Web usage mining is a new subfield of data mining research. It aims at discovery of trends and regularities in web users' access patterns. In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. A challenge in web classification is how to deal with the high dimensionality of the feature space. This paper applies the concept of rough fuzzy approach for classification in web usage mining tasks after we present Independent Component Analysis (ICA) for feature. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users.
Sentence features fusion for text summarization using fuzzy logic
(2009) Ladda Suanmali; Mohammed Salem Binwahlan; Naomie Salim; L. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.th
The scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The efficiency of the technique used for scoring the text sentences could produce good summary. The feature scores are imprecise and uncertain, this marks the differentiation between the important features and unimportant is difficult task. In this paper, we introduce fuzzy logic to deal with this problem. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 9 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries were obtained from fuzzy method. © 2009 IEEE.
Swarm Based Text Summarization
(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali; M. S. Binwahlan; Faculty of Computer Science and Information Systems, University Teknologi Malaysia, 81310 Skudai, Johor, Malaysia; email: moham2007med@yahoo.com
The scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The treating of all text features with same level of importance can be considered the main factor causing creating a summary with low quality. In this paper, we introduced a novel text summarization model based on swarm intelligence. The main purpose of the proposed model is for scoring the sentences, emphasizing on dealing with the text features fairly based on their importance. The weights obtained from the training of the model were used to adjust the text features scores, which could play an important role in the selection process of the most important sentences to be included in the final summary. The results show that the human summaries H1 and H2 are 49% similar to each other. The proposed model creates summaries which are 43% similar to the manually generated summaries, while the summaries produced by Ms Word summarizer are 39% similar. © 2009 IEEE.
Swarm diversity based text summarization
(2009) Mohammed Salem Binwahlan; Naomie Salim; Ladda Suanmali; M. S. Binwahlan; Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia; email: moham2007med@yahoo.com
Automatic text summarization systems aim to make their created summaries closer to human summaries. The summary creation under the condition of the redundancy and the summary length limitation is a challenge problem. The automatic text summarization system which is built based on exploiting of the advantages of different techniques in form of an integrated model could produce a good summary for the original document. In this paper, we introduced an integrated model for automatic text summarization problem; we tried to exploit different techniques advantages in building of our model like advantage of diversity based method which can filter the similar sentences and select the most diverse ones and advantage of the differentiation between the most important features and less important using swarm based method. The experimental results showed that our model got the best performance over all methods used in this study. © 2009 Springer-Verlag Berlin Heidelberg.
The development of cross-language plagiarism detection tool utilising fuzzy swarm-based summarisation
(2010) Salha Alzahrani; Naomie Salim; Chow Kok Kent; Mohammed Salem Binwahlan; Ladda Suanmali; S. Alzahrani; Faculty of CS and Info. Sys., Taif University, Taif, Saudi Arabia; email: s.zahrani@tu.edu.sa
This work presents the design and development of a web-based system that supports cross-language similarity analysis and plagiarism detection. A suspicious document dq in a language Lq is to be submitted to the system via a PHP web-based interface. The system will accept the text through either uploading or pasting it directly to a text-area. In order to lighten large texts and provide an ideal set of queries, we introduce the idea of query document reduction via summarisation. Our proposed system utilised a fuzzy swarm-based summarisation tool originally built in Java. Then, the summary is used as a query to find similar web resources in languages Lx other than Lq via a dictionary-based translation. Thereafter, a detailed similarity analysis across the languages Lq and L xis performed and friendly report of results is produced. Such report has global similarity score on the whole document, which assures high flexibility of utilisation. © 2010 IEEE.
Using association rules and Markov model for predict next access on Web usage mining
(2006) Siriporn Chimphlee; Naomie Salim; Mohd Salihin Bin Ngadiman; Witcha Chimphlee
Predicting the next request of a user as visits Web pages has gained importance as Web-based activity increases. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user's next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. In these approaches, sequences of user requests are collected by the session identification technique, which distinguishes the requests for the same web page in different browses. We report experimental studies using real server log for comparison between methods and show that degree of precision. © 2006 Springer.

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