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  • Open Access

    ARTICLE

    Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

    Ernesto Lee1, Furqan Rustam2, Hina Fatima Shahzad2, Patrick Bernard Washington3, Abid Ishaq3, Imran Ashraf4,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3053-3077, 2023, DOI:10.32604/csse.2023.029059

    Abstract With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful… More >

  • Open Access

    ARTICLE

    An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

    Mingyong Li1, Lirong Tang1, Longfei Ma1, Honggang Zhao1, Jinyu Hu1, Yan Wei1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604

    Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the homework test score data after… More >

  • Open Access

    ARTICLE

    Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

    Kuldeep Chouhan1, Mukesh Yadav2, Ranjeet Kumar Rout3, Kshira Sagar Sahoo4, NZ Jhanjhi5,*, Mehedi Masud6, Sultan Aljahdali6

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1113-1128, 2023, DOI:10.32604/csse.2023.030842

    Abstract Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres… More >

  • Open Access

    ARTICLE

    Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

    Sujithra Kanmani*, Surendiran Balasubramanian

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1439-1454, 2023, DOI:10.32604/csse.2023.029953

    Abstract Online reviews significantly influence decision-making in many aspects of society. The integrity of internet evaluations is crucial for both consumers and vendors. This concern necessitates the development of effective fake review detection techniques. The goal of this study is to identify fraudulent text reviews. A comparison is made on shill reviews vs. genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset. We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability. Overall, the research improves fake review screening by using various transformer models such… More >

  • Open Access

    ARTICLE

    Aspect Level Songs Rating Based Upon Reviews in English

    Muhammad Aasim Qureshi1, Muhammad Asif2, Saira Anwar3, Umar Shaukat1, Atta-ur-Rahman4, Muhammad Adnan Khan5,*, Amir Mosavi6,7,8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2589-2605, 2023, DOI:10.32604/cmc.2023.032173

    Abstract With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision… More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

    S. Saranya*, G. Usha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 339-352, 2023, DOI:10.32604/iasc.2023.031987

    Abstract Laterally with the birth of the Internet, the fast growth of mobile strategies has democratised content production owing to the widespread usage of social media, resulting in a detonation of short informal writings. Twitter is microblogging short text and social networking services, with posted millions of quick messages. Twitter analysis addresses the topic of interpreting users’ tweets in terms of ideas, interests, and views in a range of settings and fields. This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event. Although sentiment examination of… More >

  • Open Access

    ARTICLE

    Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

    Sandeep Kumar1, Muhammad Badruddin Khan2, Mozaherul Hoque Abul Hasanat2, Abdul Khader Jilani Saudagar2,*, Abdullah AlTameem2, Mohammed AlKhathami2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 897-914, 2023, DOI:10.32604/cmc.2023.031867

    Abstract Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed… More >

  • Open Access

    ARTICLE

    Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

    Ahmed Abdulhakim Al-Absi1, Dae-Ki Kang2,*, Mohammed Abdulhakim Al-Absi3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 671-691, 2023, DOI:10.32604/cmc.2023.030262

    Abstract Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

    Mohemmed Sha*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 857-868, 2023, DOI:10.32604/csse.2023.031321

    Abstract The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine… More >

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