Political Articles Categorization Based on Different Naïve Bayes Models

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

doi:10.1007/978-3-030-38752-5_23

Cited by 17 publications

(8 citation statements)

References 24 publications

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“…Rough set theory plays a big role in text classification and categorization within different areas [16][17][18][19] therefore it is utilized in this paper as a classifier. Furthermore, Naïve Bayes Classifier (NBC) [20][21][22][23][24][25] is also utilized in this paper as a probabilistic classifier, which uses Bayes' theorem as a decision rule with independent features.…”

Section: Sentiment Analysismentioning

confidence: 99%

Tourism Companies Assessment via Social Media Using Sentiment Analysis

2022

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In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.

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Section: Sentiment Analysismentioning

confidence: 99%

Tourism Companies Assessment via Social Media Using Sentiment Analysis

2022

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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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“…We also considered collecting the writings of users in global forums source of our dataset. In addition to that, Arab policy forum 5 is an example of these forums.…”

Section: B Corpus Sourcesmentioning

confidence: 99%

“…Statistical analysis is considered very effective tools to determine any research work. This kind of methods can be applied for a number of research studies that propose some techniques for carrying out comparisons among various algorithms [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Paad: Political Arabic Articles Dataset for Automatic Text Categorization

Hamed

Sadiq

Abbas

2020

ijci

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Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming.

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Political Articles Categorization Based on Different Naïve Bayes Models

Cited by 17 publications

References 24 publications

Tourism Companies Assessment via Social Media Using Sentiment Analysis

Tourism Companies Assessment via Social Media Using Sentiment Analysis

Analyzing sentiment system to specify polarity by lexicon-based

Paad: Political Arabic Articles Dataset for Automatic Text Categorization

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