A FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS USING MACHINE LEARNING CLASSIFIERS

International audienceIn recent years, the use of Internet and online comments, expressed in natural language text, have increased significantly. However, it is difficult for humans to read all these comments and classify them appropriately. Consequently, an automatic approach is required to classify the unstructured data. In this paper, we propose a framework for Arabic language comprising of three steps: pre-processing, feature extraction and machine learning classification. The main aim of the proposed framework is to exploit the combination of different Arabic linguistic features. We evaluate the framework using two benchmark Arabic tweets datasets (ASTD, ATA), which enable sentiment polarity detection in general Arabic and Jordanian dialects. Comparative simulation results show that machine learning classifiers such as Support Vector Machine (SVM), Naive Bayes, MultiLayer Perceptron (MLP) and Logistic Regression-based produce the best performance by using a combination of n-gram features from Arabic tweets datasets. Finally, we evaluate the performance of our proposed framework using an Ensemble classifier approach, with promising results

Arabic sentiment analysis using GCL-based architectures and a customized regularization function

Sentiment analysis aims to extract emotions from textual data; with the proliferation of various social media platforms and the flow of data, particularly in the Arabic language, significant challenges have arisen, necessitating the development of various frameworks to handle issues. In this paper, we firstly design an architecture called Gated Convolution Long (GCL) to perform Arabic Sentiment Analysis. GCL can overcome difficulties with lengthy sequence training samples, extracting the optimal features that help improve Arabic sentiment analysis performance for binary and multiple classifications. The proposed method trains and tests in various Arabic datasets; The results are better than the baselines in all cases. GCL includes a Custom Regularization Function (CRF), which improves the performance and optimizes the validation loss. We carry out an ablation study and investigate the effect of removing CRF. CRF is shown to make a difference of up to 5.10% (2C) and 4.12% (3C). Furthermore, we study the relationship between Modern Standard Arabic and five Arabic dialects via a cross-dialect training study. Finally, we apply GCL through standard regularization (GCL+L1, GCL+L2, and GCL+LElasticNet) and our Lnew on two big Arabic sentiment datasets; GCL+Lnew gave the highest results (92.53%) with less performance time

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).

Sentiment Analysis for the Low-Resourced Latinised Arabic "Arabizi"

The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media

Persian Sentence-level Sentiment Polarity Classification

Assigning positive and negative polarity into Persian sentences is difficult task, there are different approaches has been proposed in various languages such as English. However, there is not any approach available to identify the final polarity of the Persian sentences. In this paper, the novel approach has been proposed to detect polarity for Persian sentences using PerSent lexicon (Persian lexicon). For this, we have proposed two different algorithms to detect polarity in the sentence and finally SVM, MLP and Na¨ıve Bayes classifier has been used to evaluate the performance of the proposed method. The SVM received better results in comparison with Na¨ıve Bayes and MLP.Output Status: Forthcomin

Arabic Dialect Texts Classification

This study investigates how to classify Arabic dialects in text by extracting features which show the differences between dialects. There has been a lack of research about classification of Arabic dialect texts, in comparison to English and some other languages, due to the lack of Arabic dialect text corpora in comparison with what is available for dialects of English and some other languages. What is more, there is an increasing use of Arabic dialects in social media, so this text is now considered quite appropriate as a medium of communication and as a source of a corpus. We collected tweets from Twitter, comments from Facebook and online newspapers from five groups of Arabic dialects: Gulf, Iraqi, Egyptian, Levantine, and North African. The research sought to: 1) create a dataset of Arabic dialect texts to use in training and testing the system of classification, 2) find appropriate features to classify Arabic dialects: lexical (word and multi-word-unit) and grammatical variation across dialects, 3) build a more sophisticated filter to extract features from Arabic-character written dialect text files. In this thesis, the first part describes the research motivation to show the reason for choosing the Arabic dialects as a research topic. The second part presents some background information about the Arabic language and its dialects, and the literature review shows previous research about this subject. The research methodology part shows the initial experiment to classify Arabic dialects. The results of this experiment showed the need to create an Arabic dialect text corpus, by exploring Twitter and online newspaper. The corpus used to train the ensemble classifier and to improve the accuracy of classification the corpus was extended by collecting tweets from Twitter based on the spatial coordinate points and comments from Facebook posts. The corpus was annotated with dialect labels and used in automatic dialect classification experiments. The last part of this thesis presents the results of classification, conclusions and future work

Persian Sentence-level Sentiment Polarity Classification

Assigning positive and negative polarity into Persian sentences is difficult task, there are different approaches has been proposed in various languages such as English. However, there is not any approach available to identify the final polarity of the Persian sentences. In this paper, the novel approach has been proposed to detect polarity for Persian sentences using PerSent lexicon (Persian lexicon). For this, we have proposed two different algorithms to detect polarity in the sentence and finally SVM, MLP and Na¨ıve Bayes classifier has been used to evaluate the performance of the proposed method. The SVM received better results in comparison with Na¨ıve Bayes and MLP