E mo P ars: A Collection of 30 K Emotion-Annotated P ersian Social Media Texts (2021)

Abstract: The wide reach of social media platforms, such as Twitter, have enabled many users to share their thoughts, opinions and emotions on various topics online. The ability to detect these emotions automatically would allow social scientists, as well as, businesses to better understand responses from nations and costumers. In this study we introduce a dataset of 30,000 Persian Tweets labeled with Ekman’s six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language. In this paper, we explain the data collection and labeling scheme used for the creation of this dataset. We also analyze the created dataset, showing the different features and characteristics of the data. Among other things, we investigate co-occurrence of different emotions in the dataset, and the relationship between sentiment and emotion of textual instances. The dataset is publicly available at https://github.com/nazaninsbr/Persian-Emotion-Detection .

## introduction as humans communicate emotions through text @xcite , the creation of text-based emotion detection models are a necessity for the analysis and understanding of the content posted online, more-natural speech generation, and better humancomputer interaction tasks which are categorized as affective computing foot_0 . the lack of any other signals for emotions within text (i.e., facial expressions or body language) and the subjective nature of emotions, makes this task a challenging one. despite the availability of a number of studies on the topic of emotion recognition in nlp, they have been predominantly conducted in the english language. the unavailability of annotated data in the persian language hinders the study of emotions in this language. as a result, in this study, we introduce a dataset of 30k persian tweets labeled with ekman's six basic emotions @xcite . to the best of our knowledge this is the first publicly available emotion-labeled dataset in the persian language, which we believe will allow persian nlp to progress and start the study of emotions. the rest of this paper is structured as follows: in section 2 we provide a brief review of studies on emotions and the automatic detection of emotions. we additionally point to other datasets available on the topic in other languages. next, we discuss our data collection and labeling methods in section 3. the statistics and properties of our datasets are presented in section 4. finally, we conclude the study in section 5. ## related work even though a significant body of work is available on the topic of emotions, a commonly agreed-upon definition is still lacking @xcite . the american psychology association (apa) defines emotion as "a complex reaction pattern, involving experiential, behavioral, and physiological elements, by which an individual attempts to deal with a personally significant matter or event" (apa, (accessed december 1, 2020). the three key components of emotions, stated in the aforementioned definition, are subjective experience, physiological response, and behavioral response @xcite . subjective experience has been explored in studies looking at the effects of culture, age, and gender on the emotions people feel @xcite @xcite @xcite . the physiological response to emotions could range from sweaty palms to a churning stomach. for instance, @xcite shows that heart rate is significantly increased when people watch movies conveying fear or anger, @xcite also explores frontal eeg activation in response to anger and happiness, and @xcite reviews studies on the neurological aspects of emotion, focusing mostly on fear. in part one of @xcite , the physiological processes during emotion regulation are discussed. additionally, @xcite offers a comprehensive historical overview of studies on the neural bases of emotions. lastly, behavioral responses could include smiling, sighing, and crying. these responses are found to often depend on societal norms and individual differences @xcite @xcite . there is some debate on what basic emotions are and what we really mean by "basic". @xcite discusses this issue at length. the paper refutes the claim that there exist basic emotions out of which all other emotions are built. @xcite disagrees and offers a concrete list of characteristics that basic emotions have. nevertheless, multiple classifications of emotions have been introduced in the literature. one categorization, offered by paul ekman, suggests basic emotions to be: anger, disgust, fear, happiness, sadness and surprise @xcite . later on, however, ekman does name other emotions that could potentially be proven to be basic @xcite . another well-known classification, offered by plutchik, introduces 8 basic emotions composed of anger, fear, disgust, sadness, surprise, anticipation, trust, and joy @xcite . with the growth of social media platforms, the automatic detection of emotions through text has come into focus in recent years. several surveys of studies on emotion detection from text have been conducted, one in 2014 @xcite , and two others in 2018 @xcite . as a result, in our review, we will mainly focus on newer studies. in 2019, a shared task on the detection of emotions in textual dialogue was organized (emo, (accessed december 1, 2020; @xcite , which resulted in a wave of studies on the topic. the best model on the task achieves an f1-score of 0.79, however the best two models on the task did not submit system description papers @xcite . the third ranking model on the task @xcite , uses lexical features such as emotional intensity, valence-arousal-dominance scores @xcite , and sentiment classifiers' scores to train a light-gbm tree @xcite model which achieves a micro-averaged f1 score of 0.77. @xcite which was ranked fourth, uses a neural ensemble system made up of 4 neural models. the most used embeddingmodel among the top systems, was reported to be glove @xcite . it is important to keep in mind that achieving the same level of accuracy on our dataset will be harder for two reasons: ## data collection and labeling in this section, we begin by going over the data collection method (3.1), continuing on to explaining the labeling and validation process (3.2). the statistics of our dataset are explained in section 4. the data is publicly available on github foot_1 . ## conclusion in this study we presented a dataset of emotionlabeled persian tweets and discussed the properties of the dataset. we believe this dataset is a valuable resource for future studies in persian nlp. future work could investigate models for the task of emotion detection and investigate emotions surrounding various topics on social media platforms.