SVM based approach for opinion classification in Arabic written tweets

Bouchlaghem, Rihab; Elkhelifi, Aymen; Faiz, Rim

doi:10.1109/aiccsa.2015.7507153

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…)ﺍﻝ،‬  POS tagging involves mapping words to their tags, such as verbs, nouns and adjectives. There are different types of machine tools for POS tagging [47].…”

Section: Data Pre-processingmentioning

confidence: 99%

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi

2023

Eng. Technol. Appl. Sci. Res.

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Social media platforms have a huge impact on our daily lives. They have succeeded in attracting many people to spend time communicating and expressing themselves. Twitter is a social media platform that could be considered as a source of public opinion about products, services, and events. Sentiment analysis is the art of studying public feelings about certain topics, which may be positive, negative, or neutral. This paper provides a systematic review of Arabic tweet sentiment analysis on papers published from 2012 to 2021 in digital libraries including IEEE Explorer, Science Direct, Springer Link, and Google Scholar. The main aim of this systematic review is to investigate the trends in the topics reported and to highlight potential new research lines. To achieve that, three main stages were implemented: planning, conducting, and reporting the review. Our findings suggest the need for an open-source large Arabic tweet dataset that can be used by researchers. Also, it was found that researchers have used various classification techniques, which led to different results.

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“…)ﺍﻝ،‬  POS tagging involves mapping words to their tags, such as verbs, nouns and adjectives. There are different types of machine tools for POS tagging [47].…”

Section: Data Pre-processingmentioning

confidence: 99%

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi

2023

Eng. Technol. Appl. Sci. Res.

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“…Early traditional methods relied primarily on dictionary models to perform feature counting on textual phrases, such as computing feature values via text decomposition, keyword extraction, and so on, and then determining the opinion-orientation of the text based on the feature values. Early research not only used dictionaries to count feature information in text, but also applied machine learning methods like Support Vector Machines (SVM) [8], decision trees [9,10] and other methods, which are primarily statistical methods for the analysis of opinion guidance. Most of these methods first compute sentence features based on text features and then combine all of the features into a vector.…”

Section: Opinion Tendency Recognitionmentioning

confidence: 99%

Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

Liu

Xuan

et al. 2023

Applied Sciences

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The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition.

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“…A supervised classification using Machine Learning approach to SA was built by [22] and applied an in-house dataset of 2591 tweets and/or Facebook comments. The researchers of [23] used tweets written in modern standard Arabic (MSA) about terrorism and political events that occurred in the Arab countries and classified them manually. In [24], a Machine Learning model to evaluate Arabic tweets using two machine learning algorithms Naïve Bayes and Decision Tree was built, and around 8053 Arabic YouTube comments were collected and labeled manually by [9] and some volunteering graduate students.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021

IJATCSE

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The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

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SVM based approach for opinion classification in Arabic written tweets

Cited by 8 publications

References 7 publications

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Social Media Opinion Analysis Model Based on Fusion of Text and Structural Features

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

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