Indian Institute of Management Ahmedabad

About: Indian Institute of Management Ahmedabad is a education organization based out in Ahmedabad, India. It is known for research contribution in the topics: Emerging markets & Population. The organization has 1828 authors who have published 4011 publications receiving 59269 citations. The organization is also known as: IIMA & IIM Ahmedabad.

Alcohol, babies and the death penalty: Saving lives by analysing the shape of the brain

Abstract: Alcohol can damage the brains of unborn babies. Shape analysis can assess the damage in fetal alcohol spectrum disorders. Kanti Mardia, Fred Bookstein and John Kent explain how it works, and how it can help babies and even murderers.

Key constructs in successful IS implementation: South-East Asian experience

Abstract: Although the success factor approach has been widely used to model the interaction of Information Systems (IS) with organizations, there are inherent limitations associated with this approach. This paper attempts to address some of the concerns and extends the existing base of work on success factors by identifying the key constructs in successful IS implementation and developing a model for relating these constructs.

Strategic Human Resource Management in Small Enterprises

Abstract: The need for strategic human resource management in small enterprises is discussed. Though small enterprises might wish to keep their human resource management practices informal, they will be able to increase their productivity if there is adequate human resource planning and integration of human resource strategies with business strategies. An illustrative case study is presented of a successful small enterprise which has a mix of formal and informal human resource management functions, and which has judiciously integrated its human resource strategies with its business strategies.

A boosted SVM based sentiment analysis approach for online opinionated text

Abstract: The opinionated text available on the Internet and Web 2.0 social media has created ample research opportunities related to mining and analyzing public sentiments. At the same time, the large volume of such data poses severe data processing and sentiment extraction related challenges. Different contemporary solutions based on machine learning, dictionary, statistical, and semantic based approaches have been proposed in literature for sentiment analysis of online user-generated data. Recent research studies have proved that supervised machine learning techniques like Naive Bayes (NB) and Support Vector Machines (SVM) are very effective for sentiment based classification of opinionated text. This paper proposes a hybrid sentiment classification model based on Boosted SVM. The proposed model exploits classification performance of two techniques (Boosting and SVM) applied for the task of sentiment based classification of online reviews. The results on movies and hotel review corpora of 2000 reviews have shown that the proposed approach has succeeded in improving performance of SVM when used as a weak learner for sentiment based classification. Specifically, the results show that SVM ensemble with bagging or boosting significantly outperforms a single SVM in terms of accuracy of sentiment based classification.