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  • 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

    SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

    C. P. Thamil Selvi1,*, R. PushpaLakshmi2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2439-2456, 2023, DOI:10.32604/csse.2023.029254

    Abstract One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data… More >

  • Open Access

    ARTICLE

    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703

    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of Tweets, it is difficult to… More >

  • Open Access

    ARTICLE

    Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

    Ayman Mohamed Mostafa*

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1015-1034, 2023, DOI:10.32604/iasc.2023.028041

    Abstract Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    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 >

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