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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nusrat Jahan
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Social crisis detection using Twitter based text mining-a machine learning approach Shoaib Rahman; Nusrat Jahan; Farzana Sadia; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3957

Abstract

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market Ashikur Rahaman; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Hasan Imam Bijoy; Nusrat Jahan; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4486

Abstract

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
Co-Authors Abu Kowshir Bitto Abu Shahed Shah. Md. Nazmul Arefin Ashikur Rahaman Dilara Khanom Nitu Farzana Sadia Imran Ahmed Imran Mahmud Khalid Been Md. Badruzzaman Biplob Md. Abu Hosen Shawon Md. Arafat Rahman Talukder Md. Enam Kobir Ribon Md. Hasan Imam Bijoy Md. Tanvir Ahmed Md. Tazmim Hossain Mst. Marium Akter Mst. Tuhin Akter Rezvi Shahariar Rubayed Ahmmad Rakin Shah Mohd. Ishtiaque Ahammed Khan Ishti Shoaib Rahman