We consider automated generation of humorous texts by substitution of a single word in a given short text. In this setting, several factors that potentially contribute to the funniness of texts can be integrated into a unified framework as constraints on the lexical substitution. We discuss three types of such constraints: formal constraints concerning the similarity of sounds or spellings between the original word and the substitute, semantic or connotational constraints requiring the substitute to be a taboo word, and contextual constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies using real SMSs as the corpus indicates that taboo constraints are statistically very effective, and so is a constraint requiring that the substitution takes place at the end of the text even though the effect is smaller. The effects of individual constraints are largely cumulative. In addition, connotational taboo words and word position have a strong interaction.

In recent years, so-called big data research has become a hot topic in the social sciences. This paper explores the possibilities of big data-based research within the field of music psychology. We illustrate one methodological approach by studying involuntary musical imagery, or earworms in the social networking service Twitter. Our goal was to collect a large naturalistic and culturally diverse database of discussions and to classify the encountered expressions. We describe our method and present results from automatic data classification and sentiment analyses. Over six months, we collected over 80,000 tweets from 173 locations around the world to obtain the most diverse dataset collated to date related to involuntary musical imagery. Automated classifications categorized 51% of all tweets gathered, with over 90% accuracy in each category. The most prominent categories of discussion concerned reporting earworm experiences, hyperlinks to music, spreading general information about the phenomenon, and communicating thankfulness (sincerely or ironically) about receiving earworms. Sentiment analysis revealed a balance towards negative emotional expressions in comparison to reference data. This is the first study to show this negative appraisal tendency and to demonstrate the ‘earworm’ phenomenon on a global scale. We discuss our findings in relation to previous literature and highlight the opportunities and challenges of big data research.

The ability to associate concepts is an important factor of creativity. We investigate the power of simple word co-occurrence analysis in tasks requiring verbal creativity. We first consider the Remote Associates Test, a psychometric measure of creativity. It turns out to be very easy for computers with access to statistics from a large corpus. Next, we address generation of poetry, an act with much more complex creative aspects. We outline methods that can produce surprisingly good poems based on existing linguistic corpora but otherwise minimal amounts of knowledge about language or poetry. The success of these simple methods suggests that corpus-based approaches can be powerful tools for computational support of creativity.

Abstract-A fluent ability to associate tasks, concepts, ideas, knowledge and experiences in a relevant way is often considered an important factor of creativity, especially in problem solving. We are interested in providing computational support for discovering such creative associations.In this paper we design minimally supervised methods that can perform well in the remote associates test (RAT), a well-known psychometric measure of creativity. We show that with a large corpus of text and some relatively simple principles, this can be achieved. We then develop methods for a more general word association model that could be used in lexical creativity support systems, and which also could be a small step towards lexical creativity in computers.

We study transformational computational creativity in the context of writing songs and describe an implemented system that is able to modify its own goals and operation. With this, we contribute to three aspects of computational creativity and song generation: (1) Application-wise, songs are an interesting and challenging target for creativity, as they require the production of complementary music and lyrics. (2) Technically, we approach the problem of creativity and song generation using constraint programming. We show how constraints can be used declaratively to define a search space of songs so that a standard constraint solver can then be used to generate songs. (3) Conceptually, we describe a concrete architecture for transformational creativity where the creative (song writing) system has some responsibility for setting its own search space and goals. In the proposed architecture, a meta-level control component does this transparently by manipulating the constraints at runtime based on self-reflection of the system. Empirical experiments suggest the system is able to create songs according to its own taste.