Sentiment classification in English from sentence-level annotations of emotions regarding models of affect

Alexandre Trilla; Francesc Alías

Sentiment classification in English from sentence-level annotations of emotions regarding models of affect

Alexandre Trilla, Francesc Alías

La Salle International School of Commerce and Digital Economy

Research output: Indexed journal article › Conference article › peer-review

4 Citations (Scopus)

Abstract

This paper presents a text classifier for automatically tagging the sentiment of input text according to the emotion that is being conveyed. This system has a pipelined framework composed of Natural Language Processing modules for feature extraction and a hard binary classifier for decision making between positive and negative categories. To do so, the Semeval 2007 dataset composed of sentences emotionally annotated is used for training purposes after being mapped into a model of affect. The resulting scheme stands a first step towards a complete emotion classifier for a future automatic expressive text-to-speech synthesizer.

Original language	English
Pages (from-to)	516-519
Number of pages	4
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication status	Published - 2009
Event	10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom Duration: 6 Sept 2009 → 10 Sept 2009

Keywords

Emotion tagging
Natural language processing
Sentiment classification
Text categorization

Cite this

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title = "Sentiment classification in English from sentence-level annotations of emotions regarding models of affect",

abstract = "This paper presents a text classifier for automatically tagging the sentiment of input text according to the emotion that is being conveyed. This system has a pipelined framework composed of Natural Language Processing modules for feature extraction and a hard binary classifier for decision making between positive and negative categories. To do so, the Semeval 2007 dataset composed of sentences emotionally annotated is used for training purposes after being mapped into a model of affect. The resulting scheme stands a first step towards a complete emotion classifier for a future automatic expressive text-to-speech synthesizer.",

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AU - Alías, Francesc

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N2 - This paper presents a text classifier for automatically tagging the sentiment of input text according to the emotion that is being conveyed. This system has a pipelined framework composed of Natural Language Processing modules for feature extraction and a hard binary classifier for decision making between positive and negative categories. To do so, the Semeval 2007 dataset composed of sentences emotionally annotated is used for training purposes after being mapped into a model of affect. The resulting scheme stands a first step towards a complete emotion classifier for a future automatic expressive text-to-speech synthesizer.

AB - This paper presents a text classifier for automatically tagging the sentiment of input text according to the emotion that is being conveyed. This system has a pipelined framework composed of Natural Language Processing modules for feature extraction and a hard binary classifier for decision making between positive and negative categories. To do so, the Semeval 2007 dataset composed of sentences emotionally annotated is used for training purposes after being mapped into a model of affect. The resulting scheme stands a first step towards a complete emotion classifier for a future automatic expressive text-to-speech synthesizer.

KW - Emotion tagging

KW - Natural language processing

KW - Sentiment classification

KW - Text categorization

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M3 - Conference article

AN - SCOPUS:70450194877

SN - 2308-457X

SP - 516

EP - 519

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009

Y2 - 6 September 2009 through 10 September 2009

ER -

Sentiment classification in English from sentence-level annotations of emotions regarding models of affect

Abstract

Keywords

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