Evaluation of classification methods for Indonesian text emotion detection

Muljono, Muljono; Winarsih, Nurul Anisa Sri; Supriyanto, Catur

doi:10.1109/isemantic.2016.7873824

Cited by 27 publications

(4 citation statements)

References 11 publications

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“…For instance, in the medical field of psychiatry, detected emotional states of patients help identify those at a high risk of emotional disorders and depression [ 2 ]. Thus, there has been much research on emotion recognition using facial expressions [ 3 ], thermography [ 4 ], motion capture system [ 5 ], text [ 6 ], and speech [ 7 ]. However, these modes are difficult for representing people’s true feelings because they are sensitive to subject-specific variability.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features

Jin

Kim

2020

Sensors

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Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in research on brain-computer interfaces (BCI) and health care. To recognize cross-subject emotion from EEG data accurately, a technique capable of finding an effective representation robust to the subject-specific variability associated with EEG data collection processes is necessary. In this paper, a new method to predict cross-subject emotion using time-series analysis and spatial correlation is proposed. To represent the spatial connectivity between brain regions, a channel-wise feature is proposed, which can effectively handle the correlation between all channels. The channel-wise feature is defined by a symmetric matrix, the elements of which are calculated by the Pearson correlation coefficient between two-pair channels capable of complementarily handling subject-specific variability. The channel-wise features are then fed to two-layer stacked long short-term memory (LSTM), which can extract temporal features and learn an emotional model. Extensive experiments on two publicly available datasets, the Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU (Shanghai Jiao Tong University) Emotion EEG Dataset (SEED), demonstrate the effectiveness of the combined use of channel-wise features and LSTM. Experimental results achieve state-of-the-art classification rates of 98.93% and 99.10% during the two-class classification of valence and arousal in DEAP, respectively, with an accuracy of 99.63% during three-class classification in SEED.

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Section: Introductionmentioning

confidence: 99%

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features

Jin

Kim

2020

Sensors

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“…Classical machine learning opens up the way to learn hidden patterns in data through several mathematical models and overcome the drawbacks of lexicon based approaches in handling words with implicit emotion expressions. Most studies in this approach of textual emotion detection are designed as supervised multi-class tasks and some as multi-label/target tasks [7], with learning models like Support Vector Machine (SVM) [34], Naïve Bayes [35], multi-layer perceptron [36], logistic regression [37,38] etc. Features used across such approaches can be broadly categorized as Linguistic features [34,39], Symbol level features [32], and Affective features [32,40].…”

Section: Machine Learning Based Approachesmentioning

confidence: 99%

Readers’ affect: predicting and understanding readers’ emotions with deep learning

et al. 2022

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Emotions are highly useful to model human behavior being at the core of what makes us human. Today, people abundantly express and share emotions through social media. Technological advancements in such platforms enable sharing opinions or expressing any specific emotions towards what others have shared, mainly in the form of textual data. This entails an interesting arena for analysis; as to whether there is a disconnect between the writer’s intended emotion and the reader’s perception of textual content. In this paper, we present experiments for Readers’ Emotion Detection through multi-target regression settings by exploring a Bi-LSTM-based Attention model, where our major intention is to analyze the interpretability and effectiveness of the deep learning model for the task. To conduct experiments, we procure two extensive datasets REN-10k and RENh-4k, apart from using a popular benchmark dataset from SemEval-2007. We perform a two-phase experimental evaluation, first being various coarse-grained and fine-grained evaluations of our model performance in comparison with several baselines belonging to different categories of emotion detection, viz., deep learning, lexicon based, and classical machine learning. Secondly, we evaluate model behavior towards readers’ emotion detection assessing attention maps generated by the model through devising a novel set of qualitative and quantitative metrics. The first phase of experiments shows that our Bi-LSTM + Attention model significantly outperforms all baselines. The second analysis reveals that emotions may be correlated to specific words as well as named entities.

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“…Second, Twitter datasets for emotion recognition tasks are primarily available for the English language [9], [10]. Datasets for Indonesian emotion recognition from previous studies are not available publicly [11], [12]. Fortunately, Saputri et al share their Indonesian Twitter dataset for emotion classification task [13].…”

Section: Introductionmentioning

confidence: 99%

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Sutoyo,

Warnars,

Isa

et al. 2023

IJACSA

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Twitter is a social media platform that has a large amount of unstructured natural language text. The content of Twitter can be utilized to capture human behavior via emphasized emotions located in tweets. In their tweets, people commonly express emotions to show their feelings. Hence, it is crucial to recognize the text's underlined emotions to understand the message's meaning. Feature engineering is the process of improving raw data into often overlooked features. This research explores feature engineering techniques to find the best features for building an emotion recognition model on the Indonesian Twitter dataset. Two different text data representations were used, namely, TF-IDF and word embedding. This research proposed 12 feature engineering configurations in TF-IDF by combining data stemming, data augmentation, and machine learning classifiers. Moreover, this research proposed 27 feature engineering configurations in word embedding by combining three-word embedding models, three pooling techniques, and three machine-learning classifiers. In total, there are 39 feature engineering combinations. The configuration with the best F1 score is TF-IDF with logistic regression, stemmed dataset, and augmented dataset. The model achieved 65.27% accuracy and 66.09% F1 score. The detailed characteristics from the top seven models in TF-IDF also follow the same feature engineering configuration. Lastly, this work improves performance from the previous research by 1.44% and 2.01% on the word2vec and fastText approaches, respectively.

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Evaluation of classification methods for Indonesian text emotion detection

Cited by 27 publications

References 11 publications

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features

Readers’ affect: predicting and understanding readers’ emotions with deep learning

Indonesian Twitter Emotion Recognition Model using Feature Engineering

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