Understanding the role of Emojis for emotion detection in T amil (2022)

Abstract: of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.

## introduction the technique of recognizing a person's emotional state of mind by facial expression and demeanor is known as emotion detection (ed) @xcite . detecting a per-son's emotion in the text is difficult since it seldom provides phrases that explicitly stress the individual's feelings, and emotion is only discovered by interception of concepts through text data. ed is critical in many rapidly evolving fields including e-commerce, social media, comprehensive search, and advertising. despite past work on ed including speech and facial expressions, text-based ed is limited @xcite . furthermore, ed in tamil texts is harder than in english due to the scarcity of corpora and nlp tools for low resource languages like tamil @xcite . people comment on posts/videos on social media sites such as twitter, youtube, and instagram and express their emotions @xcite . because facial expressions cannot be observed in writing, emojis can be used to infer how the person is feeling @xcite @xcite . emoji usage is also quite widespread on social media since it allows individuals to express themselves. however, little progress has been made in comprehending the significance of emojis in ed in texts, particularly in low-resource languages like tamil. along with the fundamental emotions of fear, anger, joy, sorrow, disgust, and surprise @xcite , five additional categories of neutral, ambiguous, anticipation, love, and trust are used in this study. this research investigates tamil youtube comments to determine the emotions they represent. the primary purpose of this article is to investigate how effectively emojis assist in text emotion detection. ## prior works prior works that used transformer-based models like multilingual-bert and xlm-r to to categorize tamil youtube comments into eleven emotions demonstrate how xml-r outperformed all other models with a macro f1score of 0.33 @xcite . these multilingual transformers are also used to detect offensive hate and offensive content in tamil youtube comments @xcite . the work details how the process of stemming and affix stripping makes a difference by giving better results in bert inputs, especially in muril. prior works also focus on aspect-based and @xcite and context aware sentiment with attention-enhanced features from bidirectional transformers @xcite . various text embedding techniques/traditional algorithms are proposed, particularly for short text classification. @xcite @xcite @xcite multilingual bert models like indic bert and xlmroberta are used to detect offensive content in code-mixed hindi-english tweets. using them as embedding models with ensemble models as downstream classifiers seem to provide better performance than other classifiers @xcite . bert based approaches see their usage not only in tamil but also arabic tweets. including emoji in these approaches show an improvement in the performance of the models in identifying hate speech @xcite . the work also states that the incorporation of textual emoji descriptions as features may enhance or degrade the performance of the models, depending on the number of examples per class and whether emojis are a distinguishing characteristic between classes. in previous works, sentiment analysis and span detection is performed using transformers models in code-mixed languages like tamil-english and hindi-english @xcite @xcite . emoji embedding is one way to develop features for sentiment analysis tasks and can be seen in works that implement in bi-lstm based models for enhanced performace @xcite . other works in indian languages like hindi and marathi demonstrate the advantage of using xgboost for multiclass classification @xcite . sentiment analysis on the english twitter dataset shows that the inclusion of emojis using tf-idf as a feature extraction technique shows marginal improvement over excluding the emojis @xcite . investigations show that a cnn can be used for emotion detection in tamil when used with embedding approaches like bow, tfidf, word2vec, fasttext, and glove (andrew, 2022). moreover, there are works which focus on detecting signs of depression using xgboost and detecting abusive comments in tamil using transformer models from social media @xcite . the amount of emoji usage and the presence of text and emoji in expressing sentiments have been examined using the web documents of well-known male and female celebrities and compares the overall emoji usage among the most popular twitter users @xcite . the work demonstrates how sentiment analysis for both text and emoji is more thorough and accurate. prior works also used other deep learning models like self-attentive lstm, bilstm-crf and hybrid convolutional bidirectional recurrent neural network for sentiment analysis @xcite @xcite . ## proposed methodology understanding the function of emojis in tamil text during sentiment analysis is the primary motivation behind this work. tamil comments from youtube are used as the input texts, which are preprocessed and vectorized using tf-idf and muril. logistic regression and ensemble models like random forest and xg-boost are then trained on the features. crossvalidation is performed on the results and further analysis on the impact of emoji in text is discussed. ## results and discussion in this research, the evaluation metrics taken into account are weighted precision, weighted recall and weighted f1-score. since the dataset is imbalanced, weighted metrics are taken into account. while recall measures how effectively the positives are recognized, precision measures how accurately the predictions are made. f1score is a culmination of the values of precision and recall. it can be inferred from the it can be inferred that in both tf-idf and muril, text with emoji name has the best results. this could account to the fact that, when emojis are converted to its name state, it has more repetitive terms. for instance, the key word "heart" appears in the phrases "red heart ", "growing heart ", "sparkling heart ", "purple heart ", "blue heart ", and so forth. the meaning of a heart is the same regardless of how it is shown in an emoji. every emoji has a distinct unicode. this attributes to the fact that during vectorization, all these emojis are taken as unique features even when they have something in common. this issue is avoided when the emoji is converted to textual format, where more emphasis is given to each word while increasing the models performance. pre-trained transformer models generally perform well in nlp tasks. their ability to do nsp (next sentence prediction) is used to learn the context between words, which can be used in a variety of tasks. surprisingly googles muril transformer model trained on multilingual data including tamil, fails to perform better than its tf-idf counterpart when used as an embedding layer. the model overemphasizes one particular emotion, leading to all the predictions being that particular emotion and losing generalizability across the other emotions. this might be due to the imbalanced nature of the dataset. this case can also be viewed in vaishali ganganwar et al work where they proved that muril showed underperformance due to dataset imbalance for tamil text @xcite . figure 4 details the six most occurring emojis in the corpus taken and their occurrence across all emotions. taking a look at the distribution of the emojis one can state that these popularly used emojis though being extensively used in two or three emotions are quite ambiguous and are used in unexpected emotions. the emoji sees its main usage in the joy emotion, which is to be expected as it represents rolling on the floor in laughter. however it also sees use in categories such as ambiguous and disgust which can confidently said is not represented by the emoji. from this it can be said that even though emojis can denote the emotion of the author of the text, they cannot be solely relied on and have to be used in combination with the words in the text. this is especially true in the case of sarcasm, where the emoji might denote an emotion which is not interpreted when one reads the entire text. another interesting area to draw insights from is the number of times an emoji is repeated. one might assume that the repetition of a single emoji multiple times in a text would be a strong indicator to a particular emotion. however, from ## conclusion and future works this study investigates the influence of emojis on the detection of emotion portrayed through tamil youtube comments. text embedding was performed using tf-idf and the muril pre-trained model, while downstream classifiers included logistic regression, random forest, and xgboost. the combination of tf-idf and xgboost yielded the best results, with a weighted-average f1 score of 0.32. replacing the emoji with a word that represents it outperformed expressing it using utf encoding or deleting it entirely from the text. the most often used emojis appear on text that convey an emotion very different to the one indicated by the emojis, demonstrating that one cannot rely just on these emojis to predict the emotion, but rather utilize them in conjunction with the text as has previously been shown useful. repeated use of an emoji in the same text does not produce a greater link with any particular emotion than a single use of the same emoji as has been proved by a test of significance. because the introduction of social media and messaging applications has limited humans to utilizing text and emoticons as the primary mode of communication, this field of research has enormous promise. emojis can give insight into the emotion that the author wishes to convey, but they can also be deceptive, thus other clues are necessary. more research may be done on the distinct combination of emojis and the emotion that they convey, as well as how they vary if the emojis were present independently. the dataset's imbalance was a big impediment, and working on a balanced dataset might provide better results. a bigger dataset with similar categories of emotions can be employed in future work to generalize findings from the study.