Volume-2 Issue-1, May 2022 – Indian Journal of Data Mining (IJDM)

C1619051322

IJDM Journal — Sat, 09 Apr 2022 07:15:37 +0000

The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Unsupervised Learning Based Brand Sentiment Mining using Lexicon Approaches A Study on Amazon Alexa
Ayan Chattopadhyay¹, Mukul Basu²

¹Dr. Ayan Chattopadhyay, Associate Professor, Department of Business Administration, Army Institute of Management Kolkata, Affiliated to Maulana Abul Kalam Azad University of Technology, Kolkata (W.B), India.

²Mr. Mukul Basu, Academic Head & Management Consultant, Globnet Systems, Kolkata, India.

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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Consumer sentiment analysis has gained immense attention in the recent past. The abundance of data in today’s world, especially those generated from the social media platforms, has triggered sentiment exploration like never before. The analysis of consumer sentiments have indeed helped organizations in effective decision making worldwide. In the communication technology domain, voice activated virtual assistants (VAVAs) are one of the latest entrants and they are gaining immense popularity by the time. Brand sentiment studies on VAVAs being limited in number creates an opportunity to explore further. This study fits into the domain of sentiment mining and the purpose of the paper is to review the consumer sentiment towards the global leader brand in the voice activated virtual assistant product segment, Amazon Alexa. Of the various approaches available, the researchers chose unsupervised learning based lexicon approach to estimate the brand sentiment. Three popular lexicon based sentiment classifiers, TextBlob, VADER and AFINN, have been used in the present context for exploration purpose. To the best of the knowledge of the researchers, this research effort includes, for the first time, multiple lexicon based approaches in exploring the sentiment towards the brand Alexa. This study shows consumers to have a significantly positive sentiment towards the chosen brand. The output from the three comparative classifiers reveal similar results which also validates the robustness of the outcomes and that of the chosen methods. The study anticipates a bright sales potential of the brand. Also, the use of alternative lexicon approaches is expected to enrich the existing literature in the sentiment mining domain.

Keywords: AFINN, Text Blob, Unsupervised learning, VADER, Word Cloud.
Article of the Scope: Mining Unstructured Data

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C1618051322

IJDM Journal — Sat, 26 Mar 2022 09:44:03 +0000

Criticality Trend Analysis Based on Different Types of Accidents using Data Mining Approach
Kumari Pritee¹, R. D. Garg²

¹Kumari Pritee, Faculty, Department of Computer Science and Engineering, IIIT Ranchi. (Jharkhand) India.

²R.D.Garg, Professor, Department of Geomatics Engineering, IIT Roorkee (Uttarakhand) India.

Abstract: Safety on roads and prevention of accidents are the prime concern of any highway system. Data mining is a source of retrieval of information for knowledge discovery approach. Many data mining methodologies have been applied to accident data in the recent past years. There is need to analyze the relationship between different factors related to accidents i.e. number of persons affected by fatal, minor, grievous, non-injury, road feature (ROF), road condition (ROC), cause of accident (CAU) and vehicle responsible (VR) according to daily, fortnightly, semi-fortnightly and monthly basis. The objective of this study is divided into three sub-objectives. The First sub-objective of this study is to divide number of accident dataset of National Highway sections of Karnataka state implemented by Project Implementation Unit i.e. PIU (Bangalore, Chitradurga, Dharwad, Gulbarga, Hospet and Mangalore) during January 2012 to January 2017 collected from NHAI (National Highway Authority of India) in homogeneous clusters using K-means clustering. The second sub-objective is to reflect the relationship between different factors i.e. a number of persons affected by fatal, minor, grievous, non-injury, CAU, ROC, ROF and VR using Apriori association rule. The last sub-objective is to perform temporal trend analysis for each cluster on the basis of rules generated by Association Rule Mining.

Keywords: Road accidents, Data mining, K-means clustering Algorithm, Association Rule Mining and Temporal Trend Analysis.
Article of the Scope: Data Mining

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