Qualification: 
MBA, PGDBA, M.Tech, B-Tech
jasmine@am.amrita.edu

Jasmine T. Bhaskar currently serves as the Assistant Professor (Sr.Gr.) at the Department of Computer Science Engineering at Amrita School of Engineering, Amritapuri. She has completed M. Tech. in Computer Science and also pursued M. B. A.

Publications

Publication Type: Conference Paper

Year of Publication Title

2015

Jasmine Bhaskar, Sruthi, K., and Prof. Prema Nedungadi, “Hybrid approach for emotion classification of audio conversation based on text and speech mining”, in Proceedings of the International Conference on Information and Communication Technologies (ICICT), Procedia Computer Science, 2015.[Abstract]


This paper examines the dynamics of access and exclusion in children’s Internet use, in both private and public school spaces and interrogates the role of socioeconomic and demographic predictors as well as the schooling system in shaping Internet habits. More specifically, it explores the nature of Internet use by primary school children, mainly for education and information and attempts to understand the differences across and within two types of schools- a rural public school and an elite private school. Through in-depth interviews, this research investigates the level of computer and Internet literacy among the primary school children in the age group of 8-10 years and reports the differences observed among the various social dimensions. It attempts to stress the significance and need in today’s context to provide the opportunities for physical and material access so that disadvantaged children are not excluded from the digital opportunities. © Media Watch.

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PDF iconhybrid-approach-for-emotion-classification-of-audio-conversation-based-on-text-and-speech-mining.pdf

2014

Jasmine Bhaskar, Sruthi, K., and Prof. Prema Nedungadi, “Enhanced sentiment analysis of informal textual communication in social media by considering objective words and intensifiers”, in IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 2014, Jaipur, 2014.[Abstract]


Sentiment analysis is a valuable knowledge resource to understand collective sentiments from the Web and helps make better informed decisions. Sentiments may be positive, negative or objective and the method of assigning sentiment weights to terms and sentences are important factors in determining the accuracy of the sentiment classification. We use standard methods such as Natural Language Processing, Support Vector Machines and SentiWordNet lexical resource. Our work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet for intensifiers based on the context to the semantic of the words related to the intensifier. We also reassign some of the objective words to either positive or negative sentiment. We test our sentiment classification method with product reviews of digital cameras gathered from Amazon and ebay and shows that our method improves the prediction accuracy.

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PDF iconenhanced-sentiment-analysis-of-informal-textual-communication-in-social-media-by-considering-objective-words-and-intensifiers.pdf

Publication Type: Conference Proceedings

Year of Publication Title

2014

V Geetha Lekshmy V. and Jasmine Bhaskar, “Programming Smart Environments Using π-calculus”, Proceedings of the International Conference on Information and Communication Technologies (ICICT)2014, Procedia Computer Science, vol. 46. Science Direct, Elsevier, pp. 884-891, 2014.[Abstract]


In the realm of Internet of Things (IoT), the complexity of designing ubiquitous and smart systems with dynamically evolving structure has grown to an extent where system modelling and verification has become a real hurdle. Though, a lot of innovations have taken place in this field, there are no good mechanisms for describing and modelling smart environments in a formal manner. π -calculus, proposed by Milner is a formal language which provides strong mathematical base that can be used for modelling and verifying system requirements. In this paper, we intend to offer a contribution towards understanding the usefulness of π-calculus as a language for programming a special kind of ubiquitous application: smart environments. Here we propose a model that includes a type checker for π-calculus and an application that executes π- calculus statements and generates the graphical representation of the smart environment represented by the π- calculus statement.

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