IMS at EmoInt-2017: Emotion Intensity Prediction with Affective
            Norms, Automatically Extended Resources and Deep Learning

Köper, Maximilian; Kim, Evgeny; Klinger, Roman

doi:10.18653/v1/w17-5206

Cited by 40 publications

(25 citation statements)

References 26 publications

(22 reference statements)

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“…Zahiri and Choi (2017) predicts emotion in a TV show transcript. Abdul-Mageed and Ungar (2017) and Köper et al (2017) tries to understand emotions of tweets. Li et al (2017) learns to detect emotions on user comments in Chinese language.…”

Section: Related Workmentioning

confidence: 99%

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

Chatterjee¹,

Narahari²,

Joshi³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

206

124

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In this paper, we present the SemEval-2019 Task 3-EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes-Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.

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Section: Related Workmentioning

confidence: 99%

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

Chatterjee¹,

Narahari²,

Joshi³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

206

124

View full text Add to dashboard Cite

show abstract

“…Our work bears resemblance to the runner up in the SemEval EmoInt 2017, Köper et al (2017), who used a comparatively simple model consisting of a CNN-LSTM neural network. The difference between the models presented in this paper and the IMS system is the utilisation of lexicons, and that we take a multi-task learning approach.…”

Section: Introductionmentioning

confidence: 90%

KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets

Nyegaard-Signori¹,

Helms²,

Bjerva

et al. 2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.

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“…Nowadays, state-of-the-art classification models for emotion prediction typically take into account sequential information, for instance with recurrent neural networks or convolutional neural networks [8], [9]. Clearly, these models are able to capture information expressed in phrases, for instance modifications of an emotion phrase, like in "I am slightly unhappy."…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

Strohm¹,

Klinger

2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotionbearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear-expressing words in the scope of downtoners often express surprise.

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IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning

Cited by 40 publications

References 26 publications

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets

An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

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