International Journal of Computer and Applications

Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

https://hal-audencia.archives-ouvertes.fr/hal-01996486/document

Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

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Big data and disaster management: a systematic review and agenda for future research

Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

One of the rapidly growing social network, twitter has been infiltrated by large amounts of spam. Twitter has many potential applications across diverse areas, however the signal to noise ratio is very high because of spam, which is a major obstacle of meaningful analysis and action. It is a well-studied problem in emails; however, for tweets, it is relatively less researched. In this paper we have a set up a focused study consisting of nearly 5000 Tweets related to Indian Healthcare. An extensive study has been conducted where six classifiers have been evaluated and compared for spam detection. A simple term frequency based feature selection technique has been shown to reduce the model building time significantly. Ensemble method based on top five classifiers improve the accuracy as well as the stability of the results.

A Spam Detection Study of Tweets in Indian Healthcare

Feature Selection, one of the most important preprocessing steps in Machine Learning, is the process where we automatically or manually select those features which contribute most to our prediction variable or output in which we are interested This subset of features has some very important benefits: it reduces the computational complexity of learning algorithms, saves time, improves accuracy, and the selected features can be insightful for the people involved in the problem domain Among the different ways of performing feature selection such as filter, wrapper and hybrid, filter-based separability methods can be used as a feature ranking tool in binary classification problems, most popular being the Bhattacharya distance and the Jeffries–Matusita (JM) distance However, these measures are parametric and their computation requires knowledge of the distribution from which the samples are drawn In real life, we often come across instances where it is difficult to have an idea about the distribution of observations In this paper, we have presented a new non-parametric approach for performing feature selection called the ‘Non-Parametric Distance Measure’ The experiment with the new measure is performed over nine datasets and the results are compared with other ranking-based methods for feature selection using those datasets This experiment proves that the new box-plot-based method can provide greater accuracy and efficiency than the conventional ranking-based measures for feature selection such as the Chi-Square, Symmetric Uncertainty and Information Gain

Non-parametric Distance—A New Class Separability Measure

Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed rank based feature selection algorithms with multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost.

An effective feature subset selection approach based on Jeffries-Matusita distance for multiclass problems

With the increasing social media buzz, connectivity, interaction and power, it is highly important to utilize the insights generated from these sites for better performance, expansion, and opportunities. Police departments are leveraging these online platforms to spread awareness, connect to the mass, know their concerns and opinions to use their potential accordingly for better satisfaction. We have considered the official Facebook page maintained by Kolkata Traffic Police Department for analysis. The empirical study consists of 773 page owner posts and 595 visitors’ posts, 301 reviews, and comments on page owner posts. We analyze the user engagement statistics, popular post types, trends in traffic updates of Kolkata, and extracting sentiments of the public. Our results provide practical useful insights for the users, as well as the community who are working on the policing frameworks.

Saptarsi Goswami

Papers

A Spam Detection Study of Tweets in Indian Healthcare

Non-parametric Distance—A New Class Separability Measure

An effective feature subset selection approach based on Jeffries-Matusita distance for multiclass problems

Study of Social Media Activity of Local Traffic Police Department: Their Posting Nature, Interaction, and Reviews of the Public

A novel selective learning based transformer encoder architecture with enhanced word representation