Lexicon based feature extraction for emotion text classification

Bandhakavi, Anil; Wiratunga, Nirmalie; Deepak, P; Massie, Stewart

doi:10.1016/j.patrec.2016.12.009

Cited by 131 publications

(62 citation statements)

References 30 publications

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“…However, the resulting affect dictionary includes only general categories of mood-or emotion-related words, rather than further distinguishing the type of emotion. More recent methods operate, for instance, via mixture models (Bandhakavi et al, 2017), fuzzy clustering (Poria et al, 2014), or by incorporating word embeddings (Li et al, 2017). The precision of dictionaries can further be improved by embedding these in linguistic rules that adjust for the surrounding context.…”

Section: Lexicon-based Methodsmentioning

confidence: 99%

“…Studies have shown that random forests tends to compute faster, while support vector machines yield superior performance (Chatzakou et al, 2017). These classifiers are occasionally, but infrequently, restricted to the subset of affect cues from emotion lexicons (Bandhakavi et al, 2017). However, the more common approach relies upon general linguistic features, i. e., bag-of-words with subsequent tf-idf weighting (Alm et al, 2005;Strapparava & Mihalcea, 2007).…”

Section: Machine Learningmentioning

confidence: 99%

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Deep learning for affective computing: Text-based emotion recognition in decision support

Kratzwald

Ilić

Kraus

et al. 2018

Decision Support Systems

287

117

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Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.

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Section: Lexicon-based Methodsmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Deep learning for affective computing: Text-based emotion recognition in decision support

Kratzwald

Ilić

Kraus

et al. 2018

Decision Support Systems

287

117

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show abstract

“…Lexicons are linguistic tools for the automated analysis of text. Their most notorious uses are classification and feature extraction [5,2]. They can take many forms, the most common of which is a simple list of terms associated to a certain class of interest.…”

Section: Lexicon-based Classificationmentioning

confidence: 99%

Lexicon Induction for Interpretable Text Classification

Clos

Wiratunga

2017

Research and Advanced Technology for Digital Libraries

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“…Aman and Szpakowicz (2008) used Roget's Thesaurus along with WordNet-Affect for fine-grained emotion prediction from blog data. Bandhakavi et al (2017) propose a unigram mixture model (UMM) to create a domain-specific lexicon which performs better in extracting features than Point-wise Mutual Information and supervised Latent Dirichlet Allocation methods. Neviarouskaya et al (2007) propose a rule-based system which can handle informal texts in particular.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

Islam

Mercer

Xiao³

2019

Proceedings of the 2019 Conference of the North

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The advent of micro-blogging sites has paved the way for researchers to collect and analyze huge volumes of data in recent years. Twitter, being one of the leading social networking sites worldwide, provides a great opportunity to its users for expressing their states of mind via short messages which are called tweets. The urgency of identifying emotions and sentiments conveyed through tweets has led to several research works. It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily. In this paper, we propose a novel use of a multi-channel convolutional neural architecture which can effectively use different emotion and sentiment indicators such as hashtags, emoticons and emojis that are present in the tweets and improve the performance of emotion and sentiment identification. We also investigate the incorporation of different lexical features in the neural network model and its effect on the emotion and sentiment identification task. We analyze our model on some standard datasets and compare its effectiveness with existing techniques.

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Lexicon based feature extraction for emotion text classification

Cited by 131 publications

References 30 publications

Deep learning for affective computing: Text-based emotion recognition in decision support

Deep learning for affective computing: Text-based emotion recognition in decision support

Lexicon Induction for Interpretable Text Classification

Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

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