Emotion Analysis of Tweets Banning Education in A fghanistan (2023)

Abstract: This paper introduces the first emotion-annotated dataset for the Dari variant of Persian spoken in Afghanistan. The LetHerLearn dataset contains 7,600 tweets posted in reaction to the Taliban’s ban of women’s rights to education in 2022 and has been manually annotated according to Ekman’s emotion categories. We here detail the data collection and annotation process, present relevant dataset statistics as well as initial experiments on the resulting dataset, benchmarking a number of different neural architectures for the task of Dari emotion classification.

## introduction expression and recognition of feelings are crucial aspects of human communication and social interaction @xcite . they significantly influence our experiences and shape our cognitive abilities, making emotional intelligence an essential component of artificial intelligence @xcite . emotion analysis is a growing research area that aims to enable machines to effectively recognize, analyze and understand human feelings and thinking @xcite . unlike sentiment analysis, emotion detection usually covers a broader range of responses, detecting a variety of emotions such as anger, sadness, fear, disgust, happiness and more. online social media platforms allow people to express their views on a wide range of topics such as personal, social, political, or even commercial views. twitter is one of the rich online sources for text analysis tasks as it is concise yet abundant in emotional context. on twitter, communication is unrestricted by politics, age, culture, gender, and other barriers @xcite . in the current media landscape, knowledge about people's opinions and emotions as expressed on social media can be important for various objectives, such as customer service, online sale, the analysis of political and cultural events etc. on december 20, 2022, the taliban regime banned girls and women from pursuing education and employment in afghanistan. this announcement shocked the world and the people of afghanistan, and it was met with a serious and swift reaction from politicians and citizens of different countries, as well as the united nations, political and civil figures, women activists, and citizens of afghanistan. many expressed their feelings against the taliban's decision on twitter, facebook and other social media. in this paper, we present letherlearn: a persian dari corpus of emotion-annotated twitter data based on the collection and analysis of tweets related to the ban of education in afghanistan by the taliban regime. the goal of this work is to provide insights into people's real-time perspectives, attitudes, concerns and reactions in the face of this oppression. the paper is structured as follows. section 2 discusses related work, focusing in particular on previous work for persian, section 3 then goes on to describe the creation of the letherlearn dataset, detailing the motivation for this work, data collection, annotation and relevant statistics. section 4 presents details on modeling and results for experimental evaluations of a number of neural architectures trained and evaluated on letherlearn, and finally, section 5 concludes the paper and describes some possible avenues for future work. ## related work in recent years, research on emotion recognition from text has received increasing attention in the research community, and several annotated corpora have been created for this purpose (mohammad et al., @xcite . these corpora serve as valuable resources for researchers to develop and build emotion recognition models @xcite . while there has been significant progress in emotion recognition research from text, there are still some languages for which there is relatively little research. persian is one such language, where there is currently not much research and limited availability of these types of datasets. despite the relatively limited previous work on emotion detection in persian language, there is some work on resource creation in the related area of sentiment analysis, such as the sentipers dataset @xcite , the digikala dataset @xcite and the pars-absa dataset @xcite , all based based on iranian user comments. when it comes to the task of persian emotion detection, the armanemo dataset @xcite ) contains user opinions from social media and the dataset is annotated using a mixture of manual and automatic steps, labeling 7500 comments into the 7 classes of anger, fear, joy, hatred, sadness, surprise and others. the authors trained and evaluated a number of neural models (cnn, rnn, parsbert, xlm-roberta-base and xlm-roberta-large models) on the dataset and the best performing model was xml-robertalarge, achieving a macro-averaged f1 score of 75.39%. the emopars dataset @xcite , contains 30,000 emotional tweets collected from twitter using specific emotion-related keywords and the dataset was manually annotated into the anger, fear, happiness, hatred, sadness and wonder classes. this constitutes the most similar existing dataset to the one presented here. in the following we will discuss the rationale behind the data creation effort presented here. ## dataset creation below we detail the creation of the letherlearn dataset, we begin by discussing the demand that has motivated the creation of this dataset (3.1), the data collection method (3.2), continuing on to explaining the labeling and annotation process (3.3) and finally we provide some relevant statistics of our data set (3.4). ## conclusion we have presented letherlearn: the first dari emotion-annotated dataset of tweets collected following the taliban's ban of women's education in 2022. all data and code will be made available. 1 in future work, we would like to experiment with cross-variant persian emotion detection as well as multitask learning of sentiment and emotion.