Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

Since Web 2.0 and the freedom to share information, perspectives, and opinions on global events, services, goods, etc., most of the content on social media platforms comes from users. Social media data includes user sentiment-related subjects. Due to its complexity, ambiguity, dialects, shortage of resources, and morphological diversity, little work has been done on Arabic sentiment analysis. This study introduces a novel word embedding model, AraWord2Vec, based on Wor2Vec trained on Wikipedia and the Sentiment Benchmark dataset. We also examined how Wor2Vec embedding models improved classification tasks. Second, a novel CM_BiLSTM deep learning model called Convolutional Max Pooling and Bidirectional LSTM is presented to enhance Arabic sentiment analysis. Our experimental findings revealed the performance of all models on diverse datasets with an accuracy of 98.47% in binary classification; in multi-classes classification, we obtained a high performance of 98.92%.

The rapid growth of social networks allows users to express their views on various topics, significantly impacting Sentiment Analysis (SA) in Natural Language Processing (NLP). SA extracts valuable insights and aids decision-making based on public sentiment in various domains, particularly education. In other words, Arabic Sentiment Analysis (ASA) improves educational outcomes by assessing students’ learning progress and monitoring their performance. SA faces challenges with the Arabic language due to its dialects and complex morphology. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have advanced SA by effectively capturing relevant features, outperforming traditional Machine Learning (ML) algorithms. This paper assesses FastText word embedding with a CNN-based model for Arabic Sentiment Analysis (ASA). Our results show that the CNN model with FastText achieves strong performance in both datasets, with higher classification accuracy in the first dataset compared to the second. Which confirms the effectiveness of this proposed model to evaluate website and MOOC content by measuring learner satisfaction through forum interactions and module evaluations.

The majority of available sentiment analysis systems treat English text, which resulted in a significant number of resources to assist researchers in this field. Unfortunately, this is not the case for other languages and, in particular, for the Arabic language. Few serious research attempts are reported to build necessary resources such as datasets to serve Arabic sentiment analysis research efforts. In order to expedite building necessary huge datasets in Arabic or any other dataset-poor language, automatic translation from a rich source-language can make a viable alternative to generate the required resources for a target language. This is mainly driven by the assumption that translation usually preserves the sentiment upto certain degree. In this work, we study the viability of this approach to automatically translate a huge dataset from English to Arabic for sentiment analysis purposes. Experimental results show that sentiment analysis of both original (English) dataset and the translated one (Arabic) produce comparable results. As a result, automatically translated English datasets shall add to the required resources for building robust Arabic sentiment analysis systems. Although automated translation may yield poor translations in terms of readability and comprehension by humans, sentiment analysis systems can still predict the majority of correct sentiments. Such sentiment is preserved by key words. We believe automated translation may be considered as a viable option to produce rich and well represented datasets in order to develop robust and efficient Arabic sentiment analyzers.

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other languages like English. The proposed model tackles Arabic Sentiment Analysis (ASA) by using a DL approach. ASA is a challenging field where Arabic language has a rich morphological structure more than other languages. In this work, Long Short-Term Memory (LSTM) as a deep neural network has been used for training the model combined with word embedding as a first hidden layer for features extracting. The results show an accuracy of about 82% is achievable using DL method.

We present a case study as part of a work-in-progress project about multimodal sentiment analysis on historic German plays, taking Emilia Galotti by G. E. Lessing as our initial use case. We analyze the textual version and an audio version (audio book). We focus on ready-to-use sentiment analysis methods: For the textual component, we implement a naive lexicon-based approach and another approach that enhances the lexicon by means of several NLP methods. For the audio analysis, we use the free version of the Vokaturi tool. We compare the results of all approaches and evaluate them against the annotations of a human expert, which serves as a gold standard. For our use case, we can show that audio and text sentiment analysis behave very differently: textual sentiment analysis tends to predict sentiment as rather negative and audio sentiment as rather positive. Compared to the gold standard, the textual sentiment analysis achieves accuracies of 56% while the accuracy for audio sentiment analysis is only 32%. We discuss possible reasons for these mediocre results and give an outlook on further steps we want to pursue in the context of multimodal sentiment analysis on historic plays.

Morphologically complex languages such as Arabic pose several challenges in Natural Language Processing (NLP) due to their complexity and token sparsity. Most techniques approach the problem by transforming the words of the language from their sparse surface form representation to a less sparse form before processing. The transformation usually takes the form of a morphological analysis or a morphological segmentation. This dissertation addresses two tasks in Arabic NLP: Statistical Machine Translation(SMT) and Sentiment Analysis. To improve English-Arabic SMT, we apply segmentation on Arabic to decrease token sparsity and enhance the correspondence between tokens of the English and Arabic language. However, due to this segmentation, the translation system is limited to extracting features based on morphemes (partial words) and only outputting morphemes during decoding. Such a system lacks knowledge of the original form of the words. We further improve translation from English to Arabic by combining both segmented and desegmented views of the target language. The system can benefit from segmentation's sparsity reduction and verifies its generation of correct words. We present a language-independent technique to desegmentation that approaches the problem as a string transduction task. We propose a new algorithm that desegments the decoder's search space encoded as a lattice, thus allowing the system to use features from the desegmented view of the search space. We extend the phrase-based statistical machine translation system to allow desegmentation during the decoding process on the fly. In addition, we conduct an experimental study to verify what matters most in morphologically segmented SMT models. Our second task is sentiment analysis, where we resort to Arabic lemmatization to improve sentiment analysis of Arabic tweets and blog posts. We explore translation in the opposite direction, from Arabic into English in order to evaluate the loss of sentiment predictability when Arabic social media posts are translated to English, manually or using an SMT system. We use state-of-the-art Arabic and English sentiment Analysis systems and develop automatically generated Arabic lexicons from lemmatized tweets to improve this task.

A study of the performance of embedding methods for Arabic short-text sentiment analysis using deep learning approaches

With the development and emergence of the concept of the Web, as well as the advancement of social networks, forums, blogs and E-commerce a growing number of Arabs express their views and opinions using their native language on those platforms where we find a wealth of information that could be exploited. This information, whose size is expanding and which describes opinions, has become the subject of several research fields particularly: “Sentiment Analysis” or “Opinion Mining”. Sentiment Analysis considered a subfield of natural language processing (NLP), information retrieval and text mining. Currently, sentiment analysis is commonly used for English, while it is in its infancy in the Arabic language. The most important and critical step in feeling analysis is to select an appropriate technique for classifying feelings. In this paper, we will be reviewing the published work on the application of Deep Learning (DL) techniques to the problems of sentiment analysis. We focus on the Arabic language because of its importance, the large number of its speakers, the scarcity of Arabic resources on it and the challenges related to its work. We explain, categorize, summarize and compare the techniques offered in this area.

This work focuses on fine-grained sentiment analysis of Arabic text using recent Natural Language Processing methods. Arabic is a language rich in variation, spoken by over 400 million people, yet there is a significant lack of resources for sentiment analysis. To address these challenges, this study employs AraBERT, a model specifically fine-tuned for Arabic text. A corpus of one hundred thousand Arabic reviews across categories such as hotels, books, and movies was scraped and cleaned. These reviews were then categorized into positive, negative, and mixed sentiments. AraBERT was compared with traditional machine learning methods, including Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest. AraBERT achieved superior accuracy of 88\%, along with higher precision, recall, and F1 scores for both positive and negative sentiment classes compared to the other models. This work demonstrates that AraBERT effectively analyzes the syntactic and semantic structure of Arabic, making it a valuable tool for Arabic sentiment analysis across various applications. Future work will extend the model to handle neutral sentiments and include additional dialects to further improve its performance.

Sentiment analysis has been approached from a spectrum of methodologies, including statistical learning using labelled corpus and rule-based approach where rules may be constructed based on the observations on the lexicons as well as the output from natural language processing tools. In this paper, the experiments to transform labelled datasets by using NLP tools and subsequently performing sentiment analysis via statistical learning algorithms are detailed. In addition to the common data pre-processing prior to sentiment analysis, we represent the tokens in the datasets using Part-Of-Speech (POS) tags. The aim of the experiments is to investigate the impact of POS tags on sentiment analysis, particularly on both short texts and regular texts. The experimental results on short texts show that the combination of adjective and adverb predicts the sentiment of short texts the best. While noun is generally deemed to be neutral in sentiment polarity, the experimental results show that it helps to increase the accuracy of sentiment analysis on regular texts. Besides, the role of negation analysis in the datasets has also been investigated and reported based on the experimental results obtained.

Active learning for Arabic sentiment analysis

In order to improve the accuracy of sentiment analysis, this paper presents a new method for sentiment analysis of BiLSTM-cnn text based on self attention mechanism and dense connection. Methods BiLSTM-CNN-Attmodel was established. Firstly, BiLSTM was introduced to extract context words. Then, the convolutional neural network(CNN) is used to extract local semantic features. Combined with DenseNet dense connection module, the memory strength of the whole model is improved, and the utilization rate of weight information is enhanced. Finally, Self-attention mechanism is used to improve the ability of model mining information. This paper selects the data sets of chndenticorp and CCF2012 to train the optimal value of the DenseNet feature mapping matrix. The optimal value is brought into the model contrast experiment. In the experiment, the accuracy rate, recall rate and F value of this method are all greater than 91%, which is the highest among the models. It effectively improves the accuracy of text sentiment analysis, and has high research and practical value.

Breast cancer is one of the leading causes of cancer death in women. It frequently results in deadly outcomes due to delayed identification in advanced stages. Early detection and treatment significantly improve a breast cancer patient's chance of survival. Recent advancements in machine learning have opened up possibilities for early detection. Objective: The objective of this study was to apply and compare several machine learning (ML) algorithms to see which performed better for breast cancer prediction. Methods: To achieve the study's objectives, several steps were taken. The Wisconsin Diagnostic Breast Cancer (WDBC) has been used. Data preprocessing includes normalizing, removing outliers, and fixing missing values. The data was separated into two parts: training (80%) and a testing set (20%). Several techniques have been utilized, including Multi-Layer Perceptron (MLP), Ada Boost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), K-Nearest Neighbors algorithm, Decision Trees (DT), and Gaussian Naïve Bayes (NB). Accuracy, precision, recall, and F scores were used to evaluate performance. Result: The Multi-layer Perceptron (MLP) achieved the highest accuracy of 0.998, compared with other techniques. Evaluated: To evaluate the proposed model via comparison with available studies. This study achieved superior performance to existing works for the classification prediction of breast cancer.

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data