A Rule-Based Approach to Implicit Emotion Detection in Text

Orizu, Udochukwu; He, Yulan

doi:10.1007/978-3-319-19581-0_17

Cited by 37 publications

(22 citation statements)

References 9 publications

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“…Udochukwu and He proposed a rule-based approach toward implicit emotion detection using five emotion classes in the OCC model [43]. The approach was tested on three datasets: ISEAR, SemEval2007 and Alm.…”

Section: Emotion Analysis Techniquesmentioning

confidence: 99%

Emotions in texts

Pokhun

Chuttur

2020

Bull.Socinf.The.App

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Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into relevant research with findings that may be useful in real world applications. From our analysis and findings, we urge researchers to consider the development of datasets, evaluation benchmarks and a common platform for sharing achievements in emotion analysis to see further development in the field.

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Section: Emotion Analysis Techniquesmentioning

confidence: 99%

Emotions in texts

Pokhun

Chuttur

2020

Bull.Socinf.The.App

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“…In this paper classification performance range up to 81% and 81.7% using Naïve Bayes and logistics regression. Udochukwu et al (2015) [18] presents methods called as a rule-based pipeline which help in recognition of emotions from the text with the help of the OCC model. In this research paper, five classes of emotion have been used.…”

Section: Dipti Sharma Munish Sabharwalmentioning

confidence: 99%

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019

IJITEE

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Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

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“…Visual-based emotion detection for ''natural'' human-robot interaction (HRI) creates an appropriate reaction according to the emotional detection of the communication partner (Udochukwu and He 2015). The rationale for developing the YCEL is to keep continuously update for 4-6 years old children' emotional development average applying artificial intelligence with big-data analysis.…”

Section: Ycelmentioning

confidence: 99%

Development and evaluation of an emotional lexicon system for young children

Wei

2019

Microsyst Technol

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Traditionally, children's emotion has been assessed by teachers according to observation. We should be able to detect children's emotions using algorithmic techniques. To achieve this goal, it is necessary to develop and evaluate an emotional lexicon based on the standardized test entitled Emotional Competencies Scale for Young Children (ECSYC). The purpose of this study was to establish the criterion-related validity. The methodology of this study was to firstly develop 40 scenarios based on ECSYC. Secondly, we developed the five-level criteria. Thirdly, this study implemented observer training and calculated inter-rater consistency reliability. Fourthly, observers categorized 200 children's replies into five levels. Fifthly, this study ranked the sequence of frequency of each level and completed the emotional lexicon. The findings showed that the Spearman's rho coefficient reached up to .406*. (p = .026), which is significant, indicating that Young Children Emotional Lexicon (YCEL) and ECSYC were significantly correlated. The accuracies of the emotion detection recognizer using a bimodal emotion recognition approach achieved 46.7%, 60.85% and 78.73% for facial expression recognition, speech recognition, and a bimodal emotion recognition, respectively. Findings confirmed that the YCEL is feasible for speech recognition. The bimodal emotion recognition accuracies increased 32.03% and 17.88% compared with using a single modal of facial expression recognition and speech recognition, respectively. It is feasible to automatically detect children's emotional development and bring the norm up to date.

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A Rule-Based Approach to Implicit Emotion Detection in Text

Cited by 37 publications

References 9 publications

Emotions in texts

Emotions in texts

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

Development and evaluation of an emotional lexicon system for young children

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