Arabic sentiment analysis: Lexicon-based and corpus-based

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%.

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.

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.

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%.

Homecoming, more traditionally known as Mudik, has become a trending topic on several social media platforms as soon as the 11-day homecoming ritual ban was announced on 7 April 2021. Opinions, varying from those in favor of and against the ban, start to rapidly appear. Twitter, a social media platform which is now considered to be an extension of oneself and often used to express ones’ opinion, has become flooded with comments on the homecoming ritual ban. The swarm of opinions in the form of tweets were then used as a dataset for sentiment analysis in order to understand how people perceive the ban. The algorithm used in this research is the classification algorithm using the Support Vector Machine method. The dataset was classified into three sentiments: positive, negative, and neutral. The use of the Support Vector Machine algorithm yielded a 62% accuracy with this dataset. The sentiment analysis showed that the keyword "mudik" had a neutral sentiment for the most part. Meanwhile, results of engagement analysis show that the largest forms of engagements were retweets and liking tweets that had a neutral sentiment. When the neutral sentiment was removed, we found that the largest sentiment on the homecoming ritual ban was negative. This is likely due to the release of an addendum to the Covid-19 Handling Task Force Circular Number 13 of 2021 on 22 April 2021 that imposes more restrictions on and extends the effective dates of the restrictions related to the homecoming ritual ban; exactly one day before the data scraping of 5000 datasets on tweets from 23 April 2021 was carried out. The researcher had already sampled the tweets with the most engagements (those with the most retweets and likes). It was found that some tweets had a negative sentiment, but the model classified it as having a neutral sentiment. This may be affected by inaccuracies of dataset training as some of the tweets were in Malay rather than Indonesian. A challenge that needs to be overcome is the limited number of datasets for NLP training or sentiment analysis for the Indonesian language in comparison to that of the English language. On the other hand, this has become an opportunity for the researcher to develop a more appropriate training model.

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.

Arabic language suffers from the lack of available large datasets for machine learning and sentiment analysis applications. This work adds to the recently reported large dataset BRAD, which is the largest Book Reviews in Arabic Dataset. In this paper, we introduce HARD (Hotel Arabic-Reviews Dataset), the largest Book Reviews in Arabic Dataset for subjective sentiment analysis and machine language applications. HARD comprises of 490587 hotel reviews collected from the Booking.com website. Each record contains the review text in the Arabic language, the reviewer’s rating on a scale of 1 to 10 stars, and other attributes about the hotel/reviewer. We make available the full unbalanced dataset as well as a balanced subset. To examine the datasets, we implement six popular classifiers using Modern Standard Arabic (MSA) as well as Dialectal Arabic (DA). We test the sentiment analyzers for polarity and rating classifications. Furthermore, we implement a polarity lexicon-based sentiment analyzer. The findings confirm the effectiveness of the classifiers and the datasets. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.

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.

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.

A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications)

Sentiment Analysis (SA) is an increasingly important field of study, also known as opinion mining, as it has the ability to identify the emotional tone of a source material as being either positive, negative, or neutral. Simply, SA digests natural languages and extracts insights, making it extremely useful to gain an overview of the public opinion in a certain issue, topic, or even a product. With the vast amount of information over social network platforms, shared views, website reviews, and blogs comes the importance of SA. Arabic is a rich language with extremely complex inflectional and derivational morphology making sentiment analysis in Arabic text more challenging. In this paper, we propose a novel approach to enhance the accuracy of Arabic Sentiment Analysis (ASA). In this aspect, nine supervised machine learning algorithms have been implemented for ASA. Three of these classifiers have never been used before in ASA classification, namely, they are Ridge, Gradient Boosting, and Multi-layer Perceptron. The performance of all nine classifiers is tested using our constructed dataset. The dataset contains 6318 reviews written in different forms of Arabic language and prepared manually by gathering hotel reviews from Booking.com website.

Korean is the native and official language spoken by Chinese-Korean people, and Weibo is a social media software with a huge number of users in China. Currently, there is few studies related to sentiment analysis of Korean-language Weibo texts posted by Chinese-Korean users. In this paper, we propose a sentiment classification method for Chinese-Korean Weibo based on pre-trained language model and transfer learning. Firstly, we crawled the Chinese-Korean Weibo data from Sina Weibo and label them with sentiment to get the Chinese-Korean Weibo sentiment analysis (CKWSA) dataset. Secondly, to solve the problem of few training samples of the Chinese-Korean Weibo sentiment analysis dataset, we fine-tune the classifier based on the pre-trained Korean language model on the Korean Twitter sentiment analysis dataset to obtain the Korean Twitter sentiment classification model; and further fine-tune the model on CKWSA dataset to get Chinese-Korean Weibo sentiment classification model. The experiments show that the proposed classification method based on pre-trained language model and transfer learning has great performance, and there is an improvement compared other baselines on the Chinese-Korean Weibo sentiment analysis dataset.

A review of sentiment analysis research in Arabic language

Arabic language has very rich vocabulary. It is manifested in different forms. The formal, Modern Standard Arabic (MSA), and the informal, colloquial or dialects. Dialectical languages become important as a result of the proliferation of social networks which resulted in the vast unstructured dialectical text available on the web. Unique properties of modern standard Arabic and dialects present major challenges to build sentiment analysis systems by adopting similar models designed for the English language. In this paper, we present a supervised Arabic sentiment analysis using a bag-of-words feature. We further examine using a set of key words (lexicon) for better polarity classification. The testing of the system is carried out on the freely-available Arabic books' reviews (LABR) dataset. LABR includes both modern standard Arabic and Egyptian dialectal reviews. We used both balanced and unbalanced datasets. Clearly, the balanced data set is small in size and, henceforth, a large-scale balanced dataset is required for training of the classifier model. Further, we compared the computed predicted sentiments against the actual reviews for a specific book. Findings, by annotators, had indicated ambiguity between a review and its rating when verified alongside the predicted sentiment, which provided a more reasonable result. Moreover, working with dialects and sarcasm is exceedingly exciting. Experimental results on the adopted logistic classifier model and LABR are encouraging and promising. However, a key prerequisite is the availability of rich and well represented datasets in order to develop robust and efficient Arabic sentiment analyzers.

Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard “noisy” or “controversial” data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30% of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a “complicated” class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data.