No Joke: Understanding Public Sentiment Toward Selling and Salespeople Through Cartoon Analysis

Unflattering representations of salesmanship in mass media exist in abundance. In order to gauge the depiction of selling in mass media, this article explores the nature and public perceptions of salesmanship using editorial cartoons. A theory of cartooning suggests that editorial cartoons reflect public sentiment toward events and issues and therefore provide a useful way of measuring and tracking such sentiment over time. The criteria of narrative, location, binary struggle, normative transference, and metaphor were used as a framework to analyze 286 cartoons over a 30-year period from 1983 to 2013. The results suggest that while representations of the characteristics and behaviors of salespeople shifted very little across time periods, changes in public perceptions of seller–buyer conflict, the role of the customer, and selling techniques were observed, thus indicating that cartoons are sensitive enough to measure the portrayal of selling.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.