EmotiWord: Affective Lexicon Creation with Application to Interaction and Multimedia Data

Malandrakis, Nikos; Potamianos, Alexandros; Iosif, Elias; Narayanan, Shrikanth

doi:10.1007/978-3-642-32436-9_3

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…The approach of using a common set of ‘seed words’ has been successfully applied to affective text analysis (Turney and Littman 2002; Malandrakis et al . 2011) and warrants further research also for semantic similarity computation.…”

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confidence: 91%

“…Semantic similarity is the building block for numerous applications of natural language processing (NLP), such as grammar induction (Meng and Siu 2002) and affective text categorization (Malandrakis et al . 2011). Distributional semantic models (DSM) (Baroni and Lenci 2010) are based on the distributional hypothesis of meaning (Harris 1954) assuming that semantic similarity between words is a function of the overlap of their linguistic contexts.…”

Section: Introductionmentioning

confidence: 99%

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Similarity computation using semantic networks created from web-harvested data

Iosif¹,

Potamianos²

2013

Nat. Lang. Eng.

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We investigate language-agnostic algorithms for the construction of unsupervised distributional semantic models using web-harvested corpora. Specifically, a corpus is created from web document snippets, and the relevant semantic similarity statistics are encoded in a semantic network. We propose the notion of semantic neighborhoods that are defined using co-occurrence or context similarity features. Three neighborhood-based similarity metrics are proposed, motivated by the hypotheses of attributional and maximum sense similarity. The proposed metrics are evaluated against human similarity ratings achieving state-of-the-art results.

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mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Similarity computation using semantic networks created from web-harvested data

Iosif¹,

Potamianos²

2013

Nat. Lang. Eng.

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“…There are a variety of open source sentiment lexicons in English used by sentiment detection systems: Sen-tiWordNet [96], EmotiWord [103], the LIWC dictionary [101], WordNet Affect [97], and Sentiful [104] are some of the most widespread.…”

Section: Tablementioning

confidence: 99%

“…Thus, their main disadvantage is the difficulty of building generic extraction patterns and lexicons to extract all sentiment-related expressions contained in the data and to assign them a relevant label in varying contexts. Some authors have proposed different solutions, such as expanding the affective lexicon with new entries based on semantic similarity [103] or linear programming [106].…”

Section: Tablementioning

confidence: 99%

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Clavel

Callejas

2016

IEEE Trans. Affective Comput.

129

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The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.Index Terms-Sentiment analysis, opinion mining, embodied conversational agents (ECAs), human-agent interaction, affect, emotion, socio-affective interaction ! • C. Clavel is with the Institut Mines-Télécom, Télécom ParisTech,

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Machine Learning Methods for Opinion Mining In text: The Past and the Future

Kolovou

2019

Learning and Analytics in Intelligent Systems

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EmotiWord: Affective Lexicon Creation with Application to Interaction and Multimedia Data

Cited by 4 publications

References 13 publications

Similarity computation using semantic networks created from web-harvested data

Similarity computation using semantic networks created from web-harvested data

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Machine Learning Methods for Opinion Mining In text: The Past and the Future

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