To determine whether a document or a sentence expresses a positive or negative sentiment, three main approach types are commonly used: lexicon based approaches, machine learning (ML) based approaches and hybrid approaches. English has the greatest number of sentiment analysis studies, unlike other languages including Arabic and its dialects. More specifically, ML based sentiment analysis requires annotated data. In the case of lexicon based approaches, they typically require the availability of lexicons annotated by valence and/or intensity. One of the majors problems related to the treatment of Arabic and its dialect is the lack of the above resources. This survey is aimed to highlight the most recent resources that have been constructed and most recent advances in the context of sentiment analysis (related to English and Arabic language). It refers to about one hundred recent papers, most of which published between 2015 and 2018. These works are classified by category (as survey work vs. solution work). In the case of solution works, we focus on the construction of sentiment lexicon and corpus. We also propose new trends in Arabic sentiment analysis, mainly employing deep learning techniques.

Scripts are often dismissed as the stuff of good movies and bad politics. They codify cultural experience so rigidly that they remove our freedom of choice and become the very antithesis of creativity. Yet, mental scripts have an important role to play in our understanding of creative behaviour, since a deliberate departure from an established script can produce results that are simultaneously novel and familiar, especially when others stick to the conventional script. Indeed, creative opportunities often arise at the overlapping boundaries of two scripts that antagonistically compete to mentally organise the same situation. This work explores the computational integration of competing scripts to generate creative friction in short texts that are surprising but meaningful. Our exploration considers conventional macro-scripts – ordered sequences of actions – and the less obvious micro-scripts that operate at even the lowest levels of language. For the former, we generate plots that squeeze two scripts into a single mini-narrative; for the latter, we generate ironic descriptions that use conflicting scripts to highlight the speaker's pragmatic insincerity. We show experimentally that verbal irony requires both kinds of scripts – macro and micro – to work together to reliably generate creative sparks from a speaker's subversive intent.

Abstract 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.

Irony gives us a way to react creatively to disappointment. By allowing us to speak of a failed expectation as though it succeeded, irony stresses the naturalness of our expectation and the absurdity of its failure. The result of this playful use of language is a subtle valence shift as listeners are alerted to a gap between what is said and what is meant. But as irony is not without risks, speakers are often careful to signal an ironic intent with tone, body language, or if on Twitter, with the hashtag #irony. Yet given the subtlety of irony, we question the effectiveness of explicit marking, and empirically show how a stronger valence shift can be induced in automatically-generated creative tweets with more nuanced signals of irony.

Among forms of creative language, verbal humor has received some attention in the computational milieu. Some aspects of irony and wordplay could be experimented in automated systems. For instance, we developed a system that makes fun of existing acronyms, based mainly on lexical reasoning. The dimension of emotion in words is also starting to be understood among computational linguists. The challenge of electronic advertisements offers in particular a great opportunity for getting now deeper into creative language expression and emotion. An advertising message induces in the recipient a positive or negative attitude toward the advertised object. A prototype we have developed for advertising professionals has two steps: (i) the creative variation of familiar expressions, taking into account the effective content of the produced text, (ii) the automatic animation (semantically consistent with the affective text content) of the resulting expression, using kinetic typography techniques. In this way the ironic-affective effect is perceptually emphasized.