Emotion Detection from Text

Shivhare, Shiv Naresh; Khethawat, Saritha

doi:10.5121/csit.2012.2237

Cited by 83 publications

(28 citation statements)

References 9 publications

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“…A keyword spotting system, which takes text document as input and convert the text into tokens and after finding emotional words and their intensity [15]. This system analyzes negation presence in the text input and gives emotion.…”

Section: B Abstractpotting Techniquementioning

confidence: 99%

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Context based Emotion Analyzer for Interactive Agent

Malik¹,

Raza²,

Asif³

2017

ijacsa

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Section: B Abstractpotting Techniquementioning

confidence: 99%

“…This system analyzes negation presence in the text input and gives emotion. It also involve emotional words ontology to match emotional keywords present in the text [15].…”

Section: B Abstractpotting Techniquementioning

confidence: 99%

Context based Emotion Analyzer for Interactive Agent

Malik¹,

Raza²,

Asif³

2017

ijacsa

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show abstract

“…, both sentences express the emotion of happiness, with the first expressing it explicitly and the second implying it. Most research in the area of emotion detection focuses on explicit emotion detection [9,6]. Implicit emotion detection is a much more difficult task and the approaches which rely on emotion lexicons are inapplicable here.…”

Section: Introductionmentioning

confidence: 99%

A Rule-Based Approach to Implicit Emotion Detection in Text

Orizu

2015

Lecture Notes in Computer Science

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Abstract. Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17-30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on "Happy", "Angry-Disgust" and "Sad", even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

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“…Recently, some researches like Shivhare and Khethawat (2012) have been contributed to solve the problems of keyword based approach using machine learning. Combining keyword based and machine learning, higher accuracy is resulted compared to each of the individual approaches.…”

mentioning

confidence: 99%

“…Specification of concepts has been developed by Shivhare and Khethawat (2012) using emotion ontology. Ontologies are defined by entities, domain vocabulary, attributes and interrelationships.…”

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confidence: 99%

Frame Semantics Evolutionary Model for Emotion Detection

Haggag¹

2014

CIS

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Emotions play a significant role in identifying attitude, state, condition or mode of a particular circumstance. Textual data, in particular, involves emotional state and affective communication beside its informative contents. Emotion extraction from text has been potentially studied to stimulate and elicit articulation features. In this study, a machine learning emotion detection model is proposed for textual emotion recognition. A frame semantics approach is identified to extract knowledge from the text in an evolutionary process that improves the detection capabilities. Emotion detection process is controlled by a rule base; each of its entries is generated by pre-invoking event, action and resulting emotion state. Frame entities semantically collaborated to evaluate the frame emotion. Individual entities may arbitrary substituted by their synonyms or opposites if a candidate frame doesn't match any of the knowledge set. The proposed model proves considerable capability of recognizing emotions by referencing their semantic relations. Results showed better detection accuracy for the proposed model compared with variety of emotion approaches including keyword spotting, knowledge-based ANN and supervised machine learning models. Experiments indicated encouraging results over both binary emotion and multiple labels classifiers.

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Emotion Detection from Text

Abstract: ABSTRACT

Cited by 83 publications

References 9 publications

Context based Emotion Analyzer for Interactive Agent

Context based Emotion Analyzer for Interactive Agent

A Rule-Based Approach to Implicit Emotion Detection in Text

Frame Semantics Evolutionary Model for Emotion Detection

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