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  • Open Access


    Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

    K. Sailaja Kumar*, H. K. Manoj, D. Evangelin Geetha

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 485-499, 2023, DOI:10.32604/csse.2023.025390

    Abstract Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints. A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems. In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream. Twitter stream data is collected relevant to an event/topic like IPL- 2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume. Hive is used as a data warehouse to store the streamed tweets. Twitter analytics like… More >

  • Open Access


    A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

    Ankur Dumka1, Parag Verma2, Rajesh Singh3, Anil Kumar Bisht4, Divya Anand5,6,*, Hani Moaiteq Aljahdali7, Irene Delgado Noya6,8, Silvia Aparicio Obregon6,9

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6029-6044, 2022, DOI:10.32604/cmc.2022.024698

    Abstract Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the… More >

  • Open Access


    Deep Sentiment Learning for Measuring Similarity Recommendations in Twitter Data

    S. Manikandan1,*, P. Dhanalakshmi2, K. C. Rajeswari3, A. Delphin Carolina Rani4

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.023469

    Abstract The similarity recommendation of twitter data is evaluated by using sentiment analysis method. In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data. Convolutional neural network is applied for analyzing multimedia contents which is received from various sources. Recurrent neural network is used for handling the natural language data. The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method. In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc. The content is selected based on repetitive comments… More >

  • Open Access


    Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis

    Singamaneni Kranthi Kumar1,*, Alhassan Alolo Abdul-Rasheed Akeji2, Tiruvedula Mithun3, M. Ambika4, L. Jabasheela5, Ranjan Walia6, U. Sakthi7

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1217-1227, 2022, DOI:10.32604/iasc.2022.024311

    Abstract In recent times, stock price prediction helps to determine the future stock prices of any financial exchange. Accurate forecasting of stock prices can result in huge profits to the investors. The prediction of stock market is a tedious process which involves different factors such as politics, economic growth, interest rate, etc. The recent development of social networking sites enables the investors to discuss the stock market details such as profit, future stock prices, etc. The proper identification of sentiments posted by the investors in social media can be utilized for predicting the upcoming stock prices. With this motivation, this paper… More >

  • Open Access


    Can Twitter Sentiment Gives the Weather of the Financial Markets?

    Imen Hamraoui*, Adel Boubaker

    Journal on Big Data, Vol.3, No.4, pp. 155-173, 2021, DOI:10.32604/jbd.2021.018703

    Abstract Finance 3.0 is still in its infancy. Yet big data represents an unprecedented opportunity for finance. The massive increase in the volume of data generated by individuals every day on the Internet offers researchers the opportunity to approach the question of financial market predictability from a new perspective. In this article, we study the relationship between a well-known Twitter micro-blogging platform and the Tunisian financial market. In particular, we consider, over a 12-month period, Twitter volume and sentiment across the 22 stock companies that make up the Tunindex index. We find a relatively weak Pearson correlation and Granger causality between… More >

  • Open Access


    Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

    Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki*, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3463-3477, 2022, DOI:10.32604/cmc.2022.022508

    Abstract Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts as diaries because the platform… More >

  • Open Access


    Multilingual Sentiment Mining System to Prognosticate Governance

    Muhammad Shahid Bhatti1,*, Saman Azhar1, Abid Sohail1, Mohammad Hijji2, Hamna Ayemen1, Areesha Ramzan1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 389-406, 2022, DOI:10.32604/cmc.2022.021384

    Abstract In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment.… 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 the correlations between cryptocurrency movements… More >

  • Open Access


    Strengthening Software Make vs. Buy Decision: A Mixed-Method Approach

    Farooq Javeed1, Basit Shahzad2, Asadullah Shaikh3, Mana Saleh Al Reshan3,*, Saba Ahmad1, Hani Alshahrani3, Khairan Rajab3

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 193-205, 2022, DOI:10.32604/iasc.2022.021769

    Abstract Over the past few decades, multiple software development process models, tools, and techniques have been used by practitioners. Despite using these techniques, most software development organizations still fail to meet customer’s needs within time and budget. Time overrun is one of the major reasons for project failure. There is a need to come up with a comprehensive solution that would increase the chances of project success. However, the “make vs. buy” decision can be helpful for “in time” software development. Social media have become a popular platform for discussion of all sorts of topics, so software development is no exception.… More >

  • Open Access


    Stochastic Gradient Boosting Model for Twitter Spam Detection

    K. Kiruthika Devi1,*, G. A. Sathish Kumar2

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 849-859, 2022, DOI:10.32604/csse.2022.020836


    In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large… More >

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