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  • Open Access


    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

    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 >

  • Open Access


    Applying English Idiomatic Expressions to Classify Deep Sentiments in COVID-19 Tweets

    Bashar Tahayna, Ramesh Kumar Ayyasamy*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 37-54, 2023, DOI:10.32604/csse.2023.036648

    Abstract Millions of people are connecting and exchanging information on social media platforms, where interpersonal interactions are constantly being shared. However, due to inaccurate or misleading information about the COVID-19 pandemic, social media platforms became the scene of tense debates between believers and doubters. Healthcare professionals and public health agencies also use social media to inform the public about COVID-19 news and updates. However, they occasionally have trouble managing massive pandemic-related rumors and frauds. One reason is that people share and engage, regardless of the information source, by assuming the content is unquestionably true. On Twitter, users use words and phrases… More >

  • Open Access


    Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

    Muhammad Rizwan Rashid Rana1,*, Saif Ur Rehman1, Asif Nawaz1, Tariq Ali1, Azhar Imran2, Abdulkareem Alzahrani3, Abdullah Almuhaimeed4,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2415-2428, 2023, DOI:10.32604/csse.2023.035149

    Abstract People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate… More >

  • Open Access


    Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

    C. P. Thamil Selvi1,*, P. Muneeshwari2, K. Selvasheela3, D. Prasanna4

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3545-3555, 2023, DOI:10.32604/iasc.2023.031097

    Abstract The term sentiment analysis deals with sentiment classification based on the review made by the user in a social network. The sentiment classification accuracy is evaluated using various selection methods, especially those that deal with algorithm selection. In this work, every sentiment received through user expressions is ranked in order to categorise sentiments as informative and non-informative. In order to do so, the work focus on Query Expansion Ranking (QER) algorithm that takes user text as input and process for sentiment analysis and finally produces the results as informative or non-informative. The challenge is to convert non-informative into informative using… More >

  • Open Access


    Comparative Analysis Using Machine Learning Techniques for Fine Grain Sentiments

    Zeeshan Ahmad1, Waqas Haider Bangyal1, Kashif Nisar2,3,*, Muhammad Reazul Haque4, M. Adil Khan5

    Journal on Artificial Intelligence, Vol.4, No.1, pp. 49-60, 2022, DOI:10.32604/jai.2022.017992

    Abstract Huge amount of data is being produced every second for microblogs, different content sharing sites, and social networking. Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them. It is widely used for social media platforms to find user's sentiments about a particular topic or product. Capturing, assembling, and analyzing sentiments has been challenge for researchers. To handle these challenges, we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank (SST) dataset, based on 215,154 exclusive texts of different lengths that are… More >

  • Open Access


    AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments

    Fahim K. Sufi1,*, Musleh Alsulami2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3631-3649, 2022, DOI:10.32604/cmc.2022.026272

    Abstract The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,… More >

  • Open Access


    Deep Learning Framework for Classification of Emoji Based Sentiments

    Nighat Parveen Shaikh*, Mumtaz Hussain Mahar

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3145-3158, 2022, DOI:10.32604/cmc.2022.024843

    Abstract Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS). Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts. Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision. This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments. Proposed methodology consists upon three… More >

  • Open Access


    Stock Market Trading Based on Market Sentiments and Reinforcement Learning

    K. M. Ameen Suhail1, Syam Sankar1, Ashok S. Kumar2, Tsafack Nestor3, Naglaa F. Soliman4,*, Abeer D. Algarni4, Walid El-Shafai5, Fathi E. Abd El-Samie4,5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 935-950, 2022, DOI:10.32604/cmc.2022.017069

    Abstract Stock market is a place, where shares of different companies are traded. It is a collection of buyers’ and sellers’ stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends… More >

  • Open Access


    Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence Using Joint Sentiment Topic Detection with MEDLDA

    PCD Kalaivaani1,*, Dr. R Thangarajan2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 71-79, 2020, DOI:10.31209/2019.100000152

    Abstract Web mining is the process of integrating the information from web by traditional data mining methodologies and techniques. Opinion mining is an application of natural language processing to extract subjective information from web. Online reviews require efficient classification algorithms for analysing the sentiments, which does not perform an in–depth analysis in current methods. Sentiment classification is done at document level in combination with topics and sentiments. It is based on weakly supervised Joint Sentiment-Topic mode which extends the topic model Maximum Entropy Discrimination Latent Dirichlet Allocation by constructing an additional sentiment layer. It is assumed that topics generated are dependent… More >

  • Open Access


    Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

    Yun Wang1, Fazli Subhan2, Shahaboddin Shamshirband3, 4, *, Muhammad Zubair Asghar5, Ikram Ullah5, Ammara Habib5

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 631-655, 2020, DOI:10.32604/cmc.2020.07920

    Abstract The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction… More >

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