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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Anjana Kakoti Mahanta
Gauhati University
Computer Science & IT Electrical & Electronics Engineering

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Grouping of Twitter users according to contents of their tweets Farha Naznin; Anjana Kakoti Mahanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp876-884

Abstract

In today’s world most of the people use social networking sites such as Twitter. They share their opinions and their views. through these media. Grouping these users will help us in different ways such as product recommendation, opinion mining, characterization of users based on their way of expressing their feelings. In this work, we present a technique to group the users based on the textual contents of the tweets. This technique is based on an unsupervised approach of machine learning that is clustering. A method is presented for representing the users using vector space model and TF-IDF weight scheme. K-means algorithm is employed for grouping the users using cosine distance as a distance measure. For the evaluation of this method, we construct a Twitter user dataset by using the Twitter application programming interface (API). A new technique is also proposed for characterization of the clusters formed. The experimental results are promising and from the study, it is found that the users in the clusters formed could be well defined by using the proposed cluster characterization technique.
ACRMiner: An Incremental Approach for Finding Dense and Sparse Rectangular Regions from a 2D Interval Dataset Dwipen Laskar; Anjana Kakoti Mahanta
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4786

Abstract

In many applications, transactions are associated with intervals related to time, temperature, humidity or other similar measures.  The term "2D interval data" or "rectangle data" is used when there are two connected intervals with each transaction. Two connected intervals give rise to a rectangle. The rectangles may overlap producing regions with different density values. The density value or support of a region is the number of rectangles that contain it. A region is closed if its density is strictly bigger than any region properly containing it. For rectangle dataset, these regions are rectangular in shape.In this paper an algorithm named ACRMiner has been proposed that takes as input a sequence of rectangles and computes all closed overlapping rectangles and their density values. The algorithm is incremental and thus is suitable for dynamic environment. Depending on an input threshold the regions can be classified as dense and sparse.Here a tree-based data structure named as ACR-Tree is used. The method has been implemented and tested on synthetic and real-life datasets and results have been reported. Few applications of this algorithm have been discussed. The worst-case time complexity the algorithmis O(n5) where n is the number of input rectangles.
Co-Authors Dwipen Laskar Farha Naznin