Waqas Ahmad¹, Hikmat Ullah Khan^1,2,*, Fawaz Khaled Alarfaj^3,*, Saqib Iqbal⁴, Abdullah Mohammad Alomair³, Naif Almusallam³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3101-3124, 2023, DOI:10.32604/iasc.2023.040614

Abstract Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative, positive, or neutral while associating them with their identified aspects from the corresponding context. In this regard, prior methodologies widely utilize either word embedding or tree-based representations. Meanwhile, the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss. Generally, word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence. Besides, the tree-based structure conserves the grammatical and logical dependencies of context. In addition, the sentence-oriented word position describes… More >

Israa K. Salman Al-Tameemi^1,3, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh², Mohammad Asadpour²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997

Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual… More >

Jieyu An^1,*, Wan Mohd Nazmee Wan Zainon¹, Binfen Ding²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1673-1689, 2023, DOI:10.32604/iasc.2023.039763

Abstract Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses the power of transfer learning… More >

Zhizhong Liu^*, Bin Zhou, Lingqiang Meng, Guangyu Huang

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1963-1981, 2023, DOI:10.32604/iasc.2023.038835

Abstract Recently, multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams, which has great potential to surpass unimodal sentiment analysis. One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy. Unfortunately, existing work always considers feature-level fusion or decision-level fusion, and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion. To improve the performance of multimodal sentiment analysis, we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy (BAHFS). Firstly, we apply BiGRU to learn the unimodal features of… More >

Mingyong Li, Zheng Jiang^*, Zongwei Zhao, Longfei Ma

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900

Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model… More >

Sarab AlMuhaideb^*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368

Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to… More >

Wadhah Mohammed M. Aqlan¹, Ghassan Ahmed Ali^2,*, Khairan Rajab², Adel Rajab², Asadullah Shaikh², Fekry Olayah², Shehab Abdulhabib Saeed Alzaeemi^3,*, Kim Gaik Tay³, Mohd Adib Omar¹, Ernest Mangantig⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 665-686, 2023, DOI:10.32604/cmc.2023.039228

Abstract Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like… More >

Fares Abdulhafidh Dael^1,*, Ömer Çağrı Yavuz², Uğur Yavuz¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903

Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an interesting area. The purpose of… More >

Mamoona Humayun^1,*, Danish Javed², Nz Jhanjhi², Maram Fahaad Almufareh¹, Saleh Naif Almuayqil¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 575-591, 2023, DOI:10.32604/csse.2023.038765

Abstract Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering… More >

Sundas Rukhsar¹, Mazhar Javed Awan¹, Usman Naseem², Dilovan Asaad Zebari³, Mazin Abed Mohammed^4,*, Marwan Ali Albahar⁵, Mohammed Thanoon⁵, Amena Mahmoud⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 791-807, 2023, DOI:10.32604/csse.2023.038384

Abstract Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for… More >