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  • Open Access


    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang1, Jiazhao Chai2, Jianxiang Cao3, Jialing Ji3, Tong Yi4,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486

    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them into representative user feature vectors.… More >

  • Open Access


    Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

    Zhaoliang Wu1, Yuewei Wu1,2, Xiaoli Feng1, Jiajun Zou3, Fulian Yin1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3391-3412, 2024, DOI:10.32604/cmc.2024.047076

    Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately improving ASQP performance in the… More >

  • Open Access


    A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

    Kai Jiang, Bin Cao*, Jing Fan

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2965-2984, 2024, DOI:10.32604/cmes.2023.046348

    Abstract Multimodal sentiment analysis utilizes multimodal data such as text, facial expressions and voice to detect people’s attitudes. With the advent of distributed data collection and annotation, we can easily obtain and share such multimodal data. However, due to professional discrepancies among annotators and lax quality control, noisy labels might be introduced. Recent research suggests that deep neural networks (DNNs) will overfit noisy labels, leading to the poor performance of the DNNs. To address this challenging problem, we present a Multimodal Robust Meta Learning framework (MRML) for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously. Specifically, we… More >

  • Open Access


    Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

    Lujuan Deng, Boyi Liu*, Zuhe Li

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1157-1170, 2024, DOI:10.32604/cmc.2023.042150

    Abstract Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data. Concatenating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method. This fusion method does not utilize the correlation information between modalities. To solve this problem, this paper proposes a model based on a multi-head attention mechanism. First, after preprocessing the original data. Then, the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence. Next, the input coding sequence is fed into the transformer model for further… More >

  • Open Access


    Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

    Cheng Zhao1, Zhe Peng2, Xuefeng Lan3, Yuefeng Cen4, Zuxin Wang5,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1503-1523, 2024, DOI:10.32604/cmc.2024.039232

    Abstract The present study examines the impact of short-term public opinion sentiment on the secondary market, with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk. The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research. In this paper, a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed. The proposed method utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.… More >

  • Open Access


    From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

    Asif Khan1, Nada Boudjellal2, Huaping Zhang1,*, Arshad Ahmad3, Maqbool Khan3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3037-3055, 2023, DOI:10.32604/cmc.2023.044403

    Abstract Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data,… More >

  • Open Access


    Topic Modelling and Sentiment Analysis on YouTube Sustainable Fashion Comments

    Hsu-Hua Lee, Minh T. N. Nguyen*

    Journal of New Media, Vol.5, No.1, pp. 65-80, 2023, DOI:10.32604/jnm.2023.045792

    Abstract YouTube videos on sustainable fashion enable the public to gain basic knowledge about this concept. In this paper, we analyse user comments on YouTube videos that contain sustainable fashion content. The paper’s main objective is to help content creators and business managers effectively understand the perspectives of viewers, thus improving video quality and developing business. We analysed a dataset of 17,357 comments collected from 15 sustainable fashion YouTube videos. First, we use Latent Dirichlet Allocation (LDA), a topic modelling technique, to discover the abstract topics. In addition, we use two approaches to rank these topics: ranking based on proportion and… More >

  • Open Access


    A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

    Nadeem Malik1,*, Saud Altaf1, Muhammad Usman Tariq2, Ashir Ahmed3, Muhammad Babar4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1599-1615, 2023, DOI:10.32604/cmc.2023.040455

    Abstract The severity of traffic accidents is a serious global concern, particularly in developing nations. Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents. There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter, blogs and Facebook. Although such approaches are popular, there exists an issue of data management and low prediction accuracy. This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory (XLNet-Bi-LSTM) to predict traffic collisions based on data collected from… More >

  • Open Access


    Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

    Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Ayman Yafoz4, Heba Mohsen5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2741-2757, 2023, DOI:10.32604/csse.2023.034721

    Abstract Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented… More >

  • Open Access


    A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

    Bishrul Haq1, Sher Muhammad Daudpota1, Ali Shariq Imran2, Zenun Kastrati3,*, Waheed Noor4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638

    Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human… More >

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