Reviews

A. Barker and F. Manji, Writing for Change ‐ An Interactive Guide to Effective Writing, Writing for Science, Writing for Advocacy, CD‐ROM and Users Guide, Fahama/International Development Research Centre, Oxford, 2000. ISBN: 0–9536–9021–0, no price given. Softback (28 pages) and CD‐ROM

App Review Analytics Of Free Games Listed On Google Play

Smartphones have become popular in recent years; in turn, the number of application developers and publishers has grown rapidly. To understand users’ app preferences, many platforms such as Google Play provide different mechanism that allows users to rank apps. However, more detailed insights on user’s feelings, experiences, critiques, suggestions, or preferences are missing due to a lack of additional written comments. This research attempts to investigate the review analytics of Android games listed on Google Play using a proposed text analytic approach to extract all user reviews from game apps in Chinese. A total of 207,048 reviews of 4,268 free games from February to March 2013 are extracted and analyzed according to various metrics including game type and game attribute. The findings indicate there is high dependency between users’ gender and game type, males and females have differing opinions on game attributes. In particular, users of different game types prefer different game attributes. The results reveal product usage insights, as well as best practices for developers

Twitter Sentiment Analysis: Application for Classifying Tweets with Video Games as Keywords

The growth of microblogging services has expanded exponentially in recent years for mining user opinions. Sentiment analysis was applied to classify Twitter posts with video game titles as keywords. An analysis of the blog history, words and sentiments associated with the blog can help reveal whether the particular game is ‘violent’ and stress inducing or ‘non-violent’ and benign. An application was developed to collect and clean data. Naïve Bayes algorithm was applied to the cleaned data to determine the polarity of the words on the data to come to a conclusion whether, based on the words of the tweet, the particular game could be classified as ‘violent’ or ‘non-violent’. The results of the algorithm are analysed for accuracy, precision and recall. Deep learning models are discussed for use in future to improve accuracy

Economists\u27 Odd Stand on the Positive-Normative Distinction: A Behavioral Economics View

This chapter examines economists’ indefensible attachment to the positive-normative distinction, and suggests a behavioral economics explanation of their behavior on the subject. It reviews the origins of the distinction in Hume’s guillotine and logical positivism, and shows how they form the basis for Robbins’ understanding of value neutrality. It connects philosophers’ rejection of logical positivism to their rejection of the positive-normative distinction, explains and modifies Putnam’s view of fact-value entanglement, and identifies four main ethical value judgments that contemporary economists employ. The behavioral explanation of economists’ denial of these value judgments emphasizes loss aversion and economists’ social identity as economist

Tax compliance with uncertain income: a stochastic control model

This paper examines the compliance behaviour of a taxpayer endowed with a stochastic income, taking into account dynamical factors as public and private investments, within a stochastic control framework. Assuming logarithmic utilities and thanks to a suitable rewrite of the problem, we provide an existence and uniqueness result for the solution of the Hamilton–Jacobi–Bellman equation associated to the control problem, and we rely on a symbolic and numerical algorithm to study its solution. Moreover, we implement a Monte Carlo simulation in order to determine an estimate of the mean and the variance of the total declared income together with a confidence interval. To illustrate how the method works, we present a computational example where we assign values to the parameters. In this case we perform a sensitivity analysis, showing how the total declared income is affected by public and private investments, probability of being discovered, fine, tax rate and income uncertainty

Comparing Classification and Regression Tree and Support Vector Machine for Analyzing Sentiments for IPL 2016

Social media is incredibly popular method for expressing opinions and interacting with other people in the online world. Twitter is one of the most frequent online social media and micro blogging services. It enables users to communicate with others and get updates on topics and events that interest them. Tweets can reflect public attitude when taken in aggregation, for example during events such as IPL 2016.Machine learning makes sentiment analysis more effective. In this paper, we examine the evaluation of machine learning algorithms (CART and SVM) in R to find the public opinions about event IPL 2016