Group_5 Mis Assignment 3.docx

MANAGEMENT INFORMATION SYSTEMS

PRM 39- TERM III ASSIGNMENT-3

Twitter as a Structured Information System Submitted to: Prof H.K Misra

Submitted by: Group 5 Manoj (P39142) Suraj (P39171) Yogendra (P39176)

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E-R modelling ER Model is used to model the logical view of the system from data perspective which consists of these components:

ER diagrams are related to data structure diagrams, which focus on the relationships of elements within entities instead of relationships between entities themselves. ER diagrams also are often used in conjunction with data flow diagrams, which map out the flow of information for processes or system.

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Flow chart

A framework for Intelligent twitter data analysis with nonnegative matrix factorization: The purpose of the framework is to allow users to explore a collection of tweets by showing topics with relevance. In this way, it is easy to detect groups of tweets related to new technologies, events and other topics that are automatically discovered.

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Methodology: The framework is based on a three-stage process. The first stage is dataset creation by transforming a collection of tweets in a dataset according to the Vector Space Model. The second stage is the core of the framework, is cantered on the use of Nonnegative Matrix Factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined by applying Subtractive Clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are used in the last stage to enable intelligent data analysis. Findings: We applied the framework to a case study of three collections of tweets both with manual and automatic selection of the number of topics. Given the high sparsity of Twitter data, we also investigated the influence of different initializations mechanisms for NMF on the factorization results. Numerical comparisons could be used for clustering as it is comparable to kmeans cluster. Visual inspection of the word-clouds allowed a qualitative assessment of the results that confirmed the expected outcomes. Originality The proposed framework enables a collaborative approach between users and computers for an intelligent analysis of Twitter data. Users are faced with interpretable descriptions of tweet clusters, which can be interactively refined with few adjustable parameters. The resulting clusters can be used for intelligent selection of tweets, as well as for further analytics concerning the impact of products, events, etc. in the social network.

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