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Echoes of Persuasion: The Effect of Euphony in Persuasive Communication

While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect of phonetics has received little attention. By modeling a notion of euphony and analyzing four datasets comprising persuasive and non-persuasive sentences in different domains (political speeches, movie quotes, slogans and tweets), we explore the impact of sounds on different forms of persuasiveness. We conduct a series of analyses and prediction experiments within and across datasets. Our results highlight the positive role of phonetic devices on persuasion.

Sensicon: An Automatically Constructed Sensorial Lexicon

Connecting words with senses, namely, sight, hearing, taste, smell and touch, to comprehend the sensorial information in language is a straightforward task for humans by using commonsense knowledge. With this in mind, a lexicon associating words with senses would be crucial for the computational tasks aiming at interpretation of language. However, to the best of our knowledge, there is no systematic attempt in the literature to build such a resource. In this paper, we present a sensorial lexicon that associates English words with senses. To obtain this resource, we apply a computational method based on bootstrapping and corpus statistics. The quality of the resulting lexicon is evaluated with a gold standard created via crowdsourcing. The results show that a simple classifier relying on the lexicon outperforms two baselines on a sensory classification task, both at word and sentence level, and confirm the soundness of the proposed approach for the construction of the lexicon and the usefulness of the resource for computational applications.

A comparison of unsupervised methods to associate colors with words

Colors have a very important role on our perception of the world. We often associate colors with various concepts at different levels of consciousnes and these associations can be relevant to many fields such as education and advertisement. However, to the best of our knowledge, there are no systematic approaches to aid the automatic development of resources encoding this kind of knowledge. In this paper, we propose three computational methods based on image analysis, language models, and latent semantic analysis to automatically associate colors to words. We compare these methods against a gold standard obtained via crowdsourcing. The results show that each method is effective in capturing different aspects of word-color associations.

Heady-Lines: A Creative Generator Of Newspaper Headlines

In this paper we present Heady-Lines, a creative system that produces news headlines based on well-known expressions. The algorithm is composed of several steps that identify keywords from a news article, select an appropriate well-known expression and modify it to produce a novel one, using state-of-the-art natural language processing and linguistic creativity techniques. The system has a simple web-interface that abstracts the technical details from users and lets them concentrate on the task of producing creative headlines.

Exploring Sensorial Features for Metaphor Identification

Language is the main communication device to represent the environment and share a common understanding of the world that we perceive through our sensory organs. Therefore, each language might contain a great amount of sensorial elements to express the perceptions both in literal and figurative usage. To tackle the semantics of figurative language, several conceptual properties such as concreteness or imegeability are utilized. However, there is no attempt in the literature to analyze and benefit from the sensorial elements for figurative language processing. In this paper, we investigate the impact of sensorial features on metaphor identification. We utilize an existing lexicon associating English words to sensorial modalities and propose a novel technique to automatically discover these associations from a dependency-parsed corpus. In our experiments, we measure the contribution of the sensorial features to the metaphor identification task with respect to a state of the art model. The results demonstrate that sensorial features yield better performance and show good generalization properties.

Automation and Evaluation of the Keyword Method for Second Language Learning

In this paper, we combine existing NLP techniques with minimal supervision to build memory tips according to the keyword method, a well established mnemonic device for second language learning. We present what we believe to be the first extrinsic evaluation of a creative sentence generator on a vocabulary learning task. The results demonstrate that NLP techniques can effectively support the development of resources for second language learning.

MEANS: moving effective assonances for novice students

Vocabulary acquisition constitutes a crucial but difficult and time consuming step of learning a foreign language. There exist several teaching methods which aim to facilitate this step by providing learners with various verbal and visual tips. However, building systems based on these methods is generally very costly since it requires so many resources such as time, money and human labor. In this paper, we introduce a fully automatized vocabulary teaching system which uses state-of-the-art natural language processing (NLP) and information retrieval (IR) techniques. For each foreign word the user is willing to learn, the system is capable of automatically producing memorization tips including key- words, sentences, colors, textual animations and images.

A Computational Approach to Generate a Sensorial Lexicon

While humans are capable of building connections between words and sensorial modalities by using commonsense knowledge, it is not straightforward for machines to interpret sensorial information. To this end, a lexicon associating words with human senses, namely sight, hearing, taste, smell and touch, would be crucial. Nonetheless, to the best of our knowledge, there is no systematic attempt in the literature to build such a resource. In this paper, we propose a computational method based on bootstrapping and corpus statistics to automatically associate English words with senses. To evaluate the quality of the resulting lexicon, we create a gold standard via crowdsourcing and show that a simple classifier relying on the lexicon outperforms two baselines on a sensory classification task, both at word and sentence level. The results confirm the soundness of the proposed approach for the construction of the lexicon and the usefulness of the resource for computational applications.

Evaluating the impact of syntax and semantics on emotion recognition from text

In this paper, we systematically analyze the effect of incorporating different levels of syntactic and semantic information on the accuracy of emotion recognition from text. We carry out the evaluation in a supervised learning framework, and employ tree kernel functions as an intuitive and effective way to generate different feature spaces based on structured representations of the input data. We compare three different formalisms to encode syntactic information enriched with semantic features. These features are obtained from hand-annotated resources as well as distributional models. For the experiments, we use three datasets annotated according to the same set of emotions. Our analysis indicates that shallow syntactic information can positively interact with semantic features. In addition, we show how the three datasets can hardly be combined to learn more robust models, due to inherent differences in the linguistic properties of the texts or in the annotation.

Persuasive language and virality in social networks

This paper aims to provide new insights on the concept of virality and on its structure - especially in social networks. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread (b) virality is a phenomenon with many affective responses, i.e. under this generic term several different effects of persuasive communication are comprised. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be predicted according to content features. We further provide a class-based psycholinguistic analysis of the features salient for virality components.