Enhancing Arabic Sentiment Analysis Using CNN Approach Based on FastText Word Embedding

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%.

In recent years, Unicode has been standardized; and with the penetration of SNSs, the use of emojis has become common. Emojis, as they are also known, are most efective in expressing emotions in sentences. Sentiment analysis in natural language processing so far has involved learning by manual labeling of sentences. By using suitable emojis estimated from sentences, people might express their emotions more clearly and laconically. In this paper, we propose a new model that learns from sentences using emojis as labels, collecting Japanese tweets from Twitter as the corpus. We verify and compare multiple models based on EncoderDecoder Model of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our sophisticated experiments demonstrate that emojis are efective in expressing tweet emotion.

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.

In recent years, Deep Learning (DL) has garnered tremendous success in a variety of application domains such as sentiment analysis. In this paper, we study the impact of different preprocessing techniques on Sentiment Analysis (SA) in Arabic. Moreover, we describe the details of selecting good Arabic word embeddings using Word2Vec and FastText models. Furthermore, a DL architecture based on Convolutional Neural Networks (CNNs) is proposed. Experiments on a total of 63K book reviews show the suitability of our proposed system on Arabic SA.

Sentiment Analysis was a process for identifying whether a source of text contains certain opinions, emotions, and polarity. Twitter Sentiment Analysis was a process for identifying sentiment and polarity on tweet. Twitter Sentiment Analysis provided a way for did survey about public sentiment to product, or particular even through collections of tweet. Main problem in sentiment identifying was how to determine classification model that gave high accuracy to classifying sentiment of tweet. One of the method for classifying sentiment of tweet was Deep Learning. Convolutional Neural Network (CNN) was special type of architecture from Deep Learning that its architecture had convolution layer. Convolution layer was important for extract relevant feature from text for classifying sentiment. The objective of this research was for found out the best CNN model for classifying sentiment of tweet. By using a dataset of tweets about public opinion on the Smartfren 4G network service, we searched the best CNN model using 6 combination parameters, that is the computational eficiency method, window size, and dimension of word embedding for parameters in Word2Vec Skip-gram model, then activation function in convolution layer, dropout rate, and pool size in pooling layer for parameters in CNN. The test is done using 10-fold cross validation for each search for the best parameter value and produced the best CNN model with an accuracy value of 88,21%.

Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

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 (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.

Deep learning such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP). Previous research shows that RNN and CNN achieved significant results in extracting long and short-term dependencies. However, there is a lack of research on Persian/Dari due to low resources and with many difficulties in preprocessing. In this research paper, we proposed a novel sentiment analysis model using bidirectional GRU and CNN to address the challenges and improve sentiment knowledge in the Persian/Dari language. Firstly, a preprocessor is utilized to enhance data quality by eliminating noise. Secondly, a bidirectional Gated Recurrent Unit is used to capture long-term dependencies. Thirdly, our model(named PersiSentNet) employs CNN with max pooling to extract contextual features and reduce feature dimensionality. Gaussian Noise and Dropout are applied as regularization techniques to avoid the overfitting problem. Finally, the model’s performance is verified on two standard datasets, and the experimental results indicate that our model significantly outperforms the state-of-the-art models, highlighting its robustness and effectiveness in addressing the challenges of Persian/Dari language sentiment analysis.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

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.

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis under network environments. Compared with the traditional Natural Language Processing analysis tools, convolution neural networks can automatically learn useful features from sentences and improve the performance of the affective analysis model. However, the original convolution neural network model ignores sentence structure information which is very important for text sentiment analysis. In this paper, we add piece-wise pooling to the convolution neural network, which allows the model to obtain the sentence structure. And the main features of different sentences are extracted to analyze the emotional tendencies of the text. At the same time, the user’s feedback involves many different fields, and there is less labeled data. In order to alleviate the sparsity of the data, this paper also uses the generative adversarial network to make common feature extractions, so that the model can obtain the common features associated with emotions in different fields, and improves the model’s Generalization ability with less training data. Experiments on different datasets demonstrate the effectiveness of this method.

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

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%.

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.