Exploration of the best performance method of emotions classification for arabic tweets

Al-Hagery, Mohammed Abdullah; Alassaf, Manar; Al-kharboush, Faiza Mohammad

doi:10.11591/ijeecs.v19.i2.pp1010-1020

Cited by 12 publications

(11 citation statements)

References 41 publications

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“…For example, the TF-IDF and counter-vector functionality offer the highest precision, support vector machine and logistic regression with TF-IDF or counter vectorizer have reached the maximum precision, and many earlier publications examine the TF-IDF for functional extraction for ensemble classifications. 14,15…”

Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble approach for heterogeneous data with active learning

Salama

Abdelkader

Abdelwahab

2022

International Journal of Engineering Business Management

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At present, millions of internet users are contributing a huge amount of data. This data is extremely heterogeneous, and so, it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. Due to this massive growth, the classification of data and analysis has become an important research subject. Extracting information from this data has become a necessity. As a result, it was necessary to process these enormous volumes of data to uncover hidden information and therefore improve data analysis and, in turn, classification accuracy. In this paper, firstly, we focus on developing an ensemble machine-learning model based on active learning which identifies the most effective feature extraction strategy for heterogeneous data analysis, and compare it with traditional machine-learning algorithms. Secondly, we evaluate the proposed model during the experiments; five heterogeneous datasets from various domains were used, such as a Health Care Reform dataset, Sander Frandsen dataset, Financial Phrase Bank dataset, SMS Spam Collection dataset, and Textbook sales dataset. According to the results, the novel approach for data analysis performed better than conventional methods. Finally, the study’s findings confirmed the validity of the suggested technique, meeting the study’s goal of using ensemble methods with active learning to raise the model’s overall accuracy for effectively classifying and analyzing heterogeneous data, reducing the time and money spent training the model, and delivering superior analysis performance as well as insights into other elements of extracting information from heterogeneous data.

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Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble approach for heterogeneous data with active learning

Salama

Abdelkader

Abdelwahab

2022

International Journal of Engineering Business Management

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“…The data is further processed for cleaning and finding the sentiments or polarity of the tweets [17]. The data cleaning process involves the following steps (i) Stop words removal, (ii) Removal of HTML tags, (iii) Removal of Punctuations, (iv) Expression removal, (v) Split attached words, (vi) Standardizing word and (vii) Removal of URLs.…”

Section: Data Collection and Cleaningmentioning

confidence: 99%

A semi-automated hybrid approach to identify radicalization on social digital platform

Batra

Kumar

2022

IJEECS

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The <span>digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment</span>.

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“…Some have focused on distinguishing emotional expressions [29]- [32]. Others [33]- [37] have based on determining the polarity of the reviews whether these opinions are positive, negative, or neutral.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Alabid

Katheeth

2021

IJEECS

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A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.

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Exploration of the best performance method of emotions classification for arabic tweets

Cited by 12 publications

References 41 publications

A novel ensemble approach for heterogeneous data with active learning

A novel ensemble approach for heterogeneous data with active learning

A semi-automated hybrid approach to identify radicalization on social digital platform

Sentiment analysis of Twitter posts related to the COVID-19 vaccines

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