Special lssues
Table of Content

Advance Machine Learning for Sentiment Analysis over Various Domains and Applications

Submission Deadline: 30 June 2024 Submit to Special Issue

Guest Editors

Dr. Muhammad Asif, National Textile University, Pakistan.
Dr. Kemal Polat, Bolu Abant Izzet Baysal University, Turkey.
Dr. Osama Sohaib, American University of Ras Al Khaimah, UAE.

Summary

Dear Colleagues, nowadays due to the massive amount of digital data on different social platforms it has become very necessary to closely monitor that data produced in terms of tweets, comments, blogs writing, etc. Many countries and important organizations like defense, security, advertising agencies, feedback on different products, the popularity of politicians, extremism detection need state of the art sentiment analysis methods to uncover the hidden factors from these sentiments of people. This special issue will focus on the potential state of the art methods and techniques to uncover the patterns and knowledge from the tweets, comments and blogs. The main target of this special issue will be to present the research articles related to deep learning, transfer learning, RNN based models, CNN-based models, Hybrid neural network models, techniques in Natural Language Processing (NLP) and related areas. The issue will accept the papers related to the following areas.

 

Machine learning for sentiment analysis.

NLP for sentiment analysis.

Sentiment analysis for extremisms detection.

Sentiment analysis for product reviews.

Sentiment analysis for threat detections.

Multi-domain Sentiment Classification.

Aspect-based sentiment analysis.

Deep learning for sentiment analysis.

NN-based approach for sentiment analysis.

Syntax-Aware Aspect-Term Sentiment Analysis.

Graph-based methods.

Visualization of the sentiment aspects.

Span-Based Models for SA.

Real-Time Sentiment Analysis

Any related methods and techniques for uncovering the facts from sentiments.


Keywords

Machine learning; sentiment analysis; NLP; extremisms detection; product reviews.

Published Papers


  • Open Access

    ARTICLE

    DeBERTa-GRU: Sentiment Analysis for Large Language Model

    Adel Assiri, Abdu Gumaei, Faisal Mehmood, Touqeer Abbas, Sami Ullah
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050781
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Modern technological advancements have made social media an essential component of daily life. Social media allow individuals to share thoughts, emotions, and ideas. Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive, negative, neutral, or any other personal emotion to understand the sentiment context of the text. Sentiment analysis is essential in business and society because it impacts strategic decision-making. Sentiment analysis involves challenges due to lexical variation, an unlabeled dataset, and text distance correlations. The execution time increases due to the sequential processing of the sequence models. However,… More >

  • Open Access

    ARTICLE

    Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

    Aizaz Ali, Maqbool Khan, Khalil Khan, Rehan Ullah Khan, Abdulrahman Aloraini
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features,… More >

  • Open Access

    ARTICLE

    An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

    Yasmine M. Ibrahim, Reem Essameldin, Saad M. Darwish
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is More >

  • Open Access

    ARTICLE

    An Ingenious IoT Based Crop Prediction System Using ML and EL

    Shabana Ramzan, Yazeed Yasin Ghadi, Hanan Aljuaid, Aqsa Mahmood, Basharat Ali
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 183-199, 2024, DOI:10.32604/cmc.2024.047603
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield.… More >

  • Open Access

    ARTICLE

    RUSAS: Roman Urdu Sentiment Analysis System

    Kazim Jawad, Muhammad Ahmad, Majdah Alvi, Muhammad Bux Alvi
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1463-1480, 2024, DOI:10.32604/cmc.2024.047466
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting… More >

  • Open Access

    ARTICLE

    Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

    Awais Ahmad, Sohail Jabbar, Sheeraz Akram, Anand Paul, Umar Raza, Nuha Mohammed Alshuqayran
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to… More >

  • Open Access

    ARTICLE

    Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

    Fang Xu, Songhao Jiang, Yi Ma, Manzoor Ahmed, Zenggang Xiong, Yuanlin Lyu
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1095-1113, 2024, DOI:10.32604/cmc.2023.046577
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing More >

  • Open Access

    ARTICLE

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

    Asif Khan, Nada Boudjellal, Huaping Zhang, Arshad Ahmad, Maqbool Khan
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3037-3055, 2023, DOI:10.32604/cmc.2023.044403
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    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),… More >

  • Open Access

    ARTICLE

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

    Bishrul Haq, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Waheed Noor
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    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… More >

  • Open Access

    ARTICLE

    Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

    Arif Hussain Magsi, Ghulam Muhammad, Sajida Karim, Saifullah Memon, Zulfiqar Ali
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3131-3150, 2023, DOI:10.32604/cmc.2023.040784
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Recent advancements in the Vehicular Ad-hoc Network (VANET) have tremendously addressed road-related challenges. Specifically, Named Data Networking (NDN) in VANET has emerged as a vital technology due to its outstanding features. However, the NDN communication framework fails to address two important issues. The current NDN employs a pull-based content retrieval network, which is inefficient in disseminating crucial content in Vehicular Named Data Networking (VNDN). Additionally, VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles. Although various solutions have been proposed for detecting vehicles’ behavior, they inadequately addressed the challenges specific… More >

  • Open Access

    ARTICLE

    Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

    Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Maqbool Khan
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets… More >

  • Open Access

    ARTICLE

    Text Augmentation-Based Model for Emotion Recognition Using Transformers

    Fida Mohammad, Mukhtaj Khan, Safdar Nawaz Khan Marwat, Naveed Jan, Neelam Gohar, Muhammad Bilal, Amal Al-Rasheed
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep… More >

  • Open Access

    ARTICLE

    Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

    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
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    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… More >

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