Affective Text Variation and Animation for Dynamic Advertisement

Strapparava, Carlo; Valitutti, Alessandro; Stock, Oliviero

doi:10.1007/978-3-540-74889-2_22

Cited by 13 publications

(16 citation statements)

References 8 publications

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“…A large number of keywords are also from Wordnet-Affect. 9 For some other emotions we collected the possible triggers by selecting all the sentences with the target emotion and extracting the verbs and complements. The collected words are stored into the database with the corresponding part of speech.…”

Section: Methodsmentioning

confidence: 99%

“…Their study show that the knowledge based approach can produce high quality results with good precision while the supervised approach generated results with good recall and low precision. Cambria et al 10 utilized ConceptNet 11 and WordNet-Affect 9 in defining emotion vectors; they used clustering techniques to find the most similar emotion vector to a sentence vector and assigned that emotion to the sentence. Neviarouskaya et al 8 developed a machine learning based tool that extracts emotions from text and the extracted emotion is then used to create a 3D virtual model.…”

Section: Introductionmentioning

confidence: 99%

“…They created a lexicon containing a set of trigger words for each emotion that were mainly taken from Wordnet-Affect. 9…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid System for Emotion Extraction from Suicide Notes

Nikfarjam

Emadzadeh

González

2012

Biomed Inform Insights

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The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence’s clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid System for Emotion Extraction from Suicide Notes

Nikfarjam

Emadzadeh

González

2012

Biomed Inform Insights

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show abstract

“…bag of words. Many works have focused on the development of such emotional features, including the manually annotated emotion lexicons , the learning of emotion lexicons based on word similarities , and the construction of emotion lexicons based on the statistics of human annotations in Mechanical Turk tasks . The emotion lexicons built from manual annotations were accurate but limited in size, while the lexicons based on word similarity consisted of large number of words but would require further verification before usage.…”

Section: Related Workmentioning

confidence: 99%

Semisupervised learning of author‐specific emotions in micro‐blogs

Kang

Ren

2016

IEEJ Transactions Elec Engng

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Learning emotions from texts has been an active research topic in affective computing. However, the lack of reliable connection between emotions and language features has caused severely biased emotion predictions. Moreover, the author-specific patterns in emotion expression could potentially affect emotion predictions, which has never been studied. In this paper, we propose a semisupervised learning algorithm to learn emotional features from large-scaled micro-blog documents with a Bayesian network, and introduce an emotion transition factor to generate the author-specific emotion predictions. We infer the author-specific emotions in micro-blog streams through belief propagation, and learn the emotional features through an expectation maximization estimation procedure. We report results of single-label and multilabel emotion predictions on a micro-blog stream corpus, and analyze the improvements achieved by the semisupervised feature learning strategy and the incorporation of emotion transition patterns. Finally, we perform personality analysis based on the authors' emotion distribution and examine emotion distributions in the learned features.

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“…It is clear that AI is a long way from the automated writing of comedy scripts or writing gags for comedians, but there have been some proposals for the use of humor for real‐world tasks. Strapparava, Valitutti, and Stock (2007) describe some explorations of the possibility of using computational pun making in the “creative” development of an advertising campaign, but as yet that area is unexploited. The two main types of application that have been advocated and at least tentatively explored are friendlier user interfaces and educational software.…”

Section: Practical Applicationsmentioning

confidence: 99%

Can Computers Create Humor?

Ritchie

2009

AI Magazine

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Despite the fact that AI has always been adventurous in trying to elucidate complex aspects of human behaviour, only recently has there been research into computational modelling of humor. One obstacle to progress is the lack of a precise and detailed theory of how humor operates. Nevertheless, since the early 1990s, there have been a number of small programs that create simple verbal humor, and more recently there have been studies of the automatic classification of the humorous status of texts. In addition, there are a number of advocates of the practical uses of computational humor: in user-interfaces, in education, and in advertising. Computer-generated humor is still quite basic, but it could be viewed as a form of exploratory creativity. For computational humor to improve, some hard problems in AI will have to be addressed.

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Affective Text Variation and Animation for Dynamic Advertisement

Cited by 13 publications

References 8 publications

A Hybrid System for Emotion Extraction from Suicide Notes

A Hybrid System for Emotion Extraction from Suicide Notes

Semisupervised learning of author‐specific emotions in micro‐blogs

Can Computers Create Humor?

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