Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction

Hamed, Dhafar; Sadiq, Ahmed T.; Abbas, Ayad R.

doi:10.1007/978-3-030-38752-5_7

Cited by 28 publications

(14 citation statements)

References 26 publications

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“…A number of studied used methods for movie review employ a classifier taken from the machine learning field, which has been made proficient on features of movie review [11]. Machine learning approach can be classified into three categories [12,13]: unsupervised, supervised and semi-supervised approaches. These are employed to recognise expressions of polarity in text automatically, such as negative and positive.…”

Section:  Issn: 2302-9285mentioning

confidence: 99%

Analyzing sentiment system to specify polarity by lexicon-based

Hamed¹,

Abbas

Sadiq³

2021

Bulletin EEI

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Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.

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Section:  Issn: 2302-9285mentioning

confidence: 99%

Analyzing sentiment system to specify polarity by lexicon-based

Hamed¹,

Abbas

Sadiq³

2021

Bulletin EEI

Self Cite

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“…Support Vector Machines are widely used and popularly employed as classifiers [28]. The basic concept behind SVM is to employ hyperplanes to separate dissimilar classes.…”

Section: B Support Vector Machinesmentioning

confidence: 99%

Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

et al. 2021

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Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.

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“…The gamma parameter is used in some of these kernels. The most common kernels that used this parameter are polynomial, radial basis function (RBF) and sigmoid as explained below [25,26]. Kernel Functions:…”

Section: Kernel Functions Of Svmmentioning

confidence: 99%

The effect of gamma value on support vector machine performance with different kernels

Al-Mejibli

Alwan

Hamed

2020

IJECE

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Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.

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Classifying Political Arabic Articles Using Support Vector Machine with Different Feature Extraction

Cited by 28 publications

References 26 publications

Analyzing sentiment system to specify polarity by lexicon-based

Analyzing sentiment system to specify polarity by lexicon-based

Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

The effect of gamma value on support vector machine performance with different kernels

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