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  • Open Access


    MELex: The Construction of Malay-English Sentiment Lexicon

    Nurul Husna Mahadzir1, Mohd Faizal Omar2, Mohd Nasrun Mohd Nawi3,*, Anas A. Salameh4, Kasmaruddin Che Hussin5, Abid Sohail6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1789-1805, 2022, DOI:10.32604/cmc.2022.021131

    Abstract Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions. Our approach differs from previous works in that MELex can analyze text More >

  • Open Access


    A Novel Cryptocurrency Prediction Method Using Optimum CNN

    Syed H. Hasan1, Syeda Huyam Hasan2, Mohammed Salih Ahmed3, Syed Hamid Hasan4,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1051-1063, 2022, DOI:10.32604/cmc.2022.020823

    Abstract In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining More >

  • Open Access


    A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

    Muhammad Aasim Qureshi1,*, Muhammad Asif1, Mohd Fadzil Hassan2, Ghulam Mustafa1, Muhammad Khurram Ehsan1, Aasim Ali1, Unaza Sajid1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544

    Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of… More >

  • Open Access


    An Optimized Deep Learning Model for Emotion Classification in Tweets

    Chinu Singla1, Fahd N. Al-Wesabi2,3, Yash Singh Pathania1, Badria Sulaiman Alfurhood4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5, Manar Ahmed Hamza5, Mohammad Mahzari6

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6365-6380, 2022, DOI:10.32604/cmc.2022.020480

    Abstract The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly… More >

  • Open Access


    Sentiment Analysis on Social Media Using Genetic Algorithm with CNN

    Dharmendra Dangi*, Amit Bhagat, Dheeraj Kumar Dixit

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5399-5419, 2022, DOI:10.32604/cmc.2022.020431

    Abstract There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging sites. Today, customers throughout the world share their points of view on all kinds of topics through these sources. The massive volume of data created by these customers makes it impossible to analyze such data manually. Therefore, an efficient and intelligent method for evaluating social media data and their divergence needs to be developed. Today, various types of equipment and techniques are available for automatically estimating the classification of sentiments. Sentiment analysis involves determining people's emotions using… More >

  • Open Access


    COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis

    Ahmed F. Ibrahim1, M. Hassaballah2, Abdelmgeid A. Ali3, Yunyoung Nam4,*, Ibrahim A. Ibrahim3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2507-2524, 2022, DOI:10.32604/cmc.2022.018131

    Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short… More >

  • Open Access


    Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

    Norjihan Binti Abdul Ghani1,*, Suraya Hamid1, Muneer Ahmad1, Younes Saadi1, N.Z. Jhanjhi2, Mohammed A. Alzain3, Mehedi Masud4

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 913-926, 2022, DOI:10.32604/csse.2022.018467

    Abstract The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific treatment. Early and accurate detection of spread in affected regions can save precious lives. Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams. However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable… More >

  • Open Access


    Sentiment Analytics: Extraction of Challenging Influencing Factors from COVID-19 Pandemics

    Mahmoud Oglah Al Hasan Baniata*, Sohail Asghar

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 821-836, 2021, DOI:10.32604/iasc.2021.018612

    Abstract The advancement in electronic devices and communication technologies in social media have introduced major changes in today’s communication and people have accepted such communicational habits at a rapid pace. The changes involve the way people started interacting with each other, and modern mean of discovering new groups of people, and individuals with similar mindsets, mutual interests, and ideas to share with. As far as the communities are concerned, there are so many social drives (such as “Say No to Plastic”) that need to be discussed on a certain platform for their promotion. Although, it’s quit… More >

  • Open Access


    Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

    Abdullah Muhammad, Salwani Abdullah, Nor Samsiah Sani*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1783-1799, 2021, DOI:10.32604/cmc.2021.018593

    Abstract Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning… More >

  • Open Access


    Extraction of Opinion Target Using Syntactic Rules in Urdu Text

    Toqir A. Rana1,*, Bahrooz Bakht1, Mehtab Afzal1, Natash Ali Mian2, Muhammad Waseem Iqbal3, Abbas Khalid1, Muhammad Raza Naqvi4

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 839-853, 2021, DOI:10.32604/iasc.2021.018572

    Abstract Opinion target or aspect extraction is the key task of aspect-based sentiment analysis. This task focuses on the extraction of targeted words or phrases against which a user has expressed his/her opinion. Although, opinion target extraction has been studied extensively in the English language domain, with notable work in other languages such as Chinese, Arabic etc., other regional languages have been neglected. One of the reasons is the lack of resources and available texts for these languages. Urdu is one, with millions of native and non-native speakers across the globe. In this paper, the Urdu… More >

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