P inealai_ S tress I dent_ LT - EDI @ EACL 2024: Minimal configurations for Stress Identification in T amil and T elugu (2024)

Abstract: This paper introduces an approach to stress identification in Tamil and Telugu, leveraging traditional machine learning models—Fasttext for Tamil and Naive Bayes for Telugu—yielding commendable results. The study highlights the scarcity of annotated data and recognizes limitations in phonetic features relevant to these languages, impacting precise information extraction. Our models achieved a macro F1 score of 0.77 for Tamil and 0.72 for Telugu with Fasttext and Naive Bayes, respectively. While the Telugu model secured the second rank in shared tasks, ongoing research is crucial to unlocking the full potential of stress identification in these languages, necessitating the exploration of additional features and advanced techniques specified in the discussions and limitations section.

## introduction in natural language processing (nlp), a diverse range of tasks is undertaken to comprehend and process human language. these tasks encompass the intricate understanding of emotional nuances, from identifying hate speech @xcite ; @xcite to recognizing hope speech @xcite and stress detection. this broad spectrum of tasks includes text classification, named entity recognition, machine translation, text generation, question answering, text summarization, and part-of-speech tagging. the evolution of nlp models has transitioned from traditional methods such as rule-based systems and statistical models to sophisticated deep learning architectures. notable examples of these architectures include recurrent neural networks (rnns), and long short-term memory networks (lstms) @xcite . stress, simply characterized as a negative emotional response stemming from external factors, notably societal pressures, represents a significant facet of the human experience. @xcite underscores the increasing trend of individuals sharing their feelings on the internet. while substantial research has explored the identification of such emotions @xcite , particularly in languages like english @xcite ; nagle and sharma ("2018"), there exists a noticeable gap in studies focused on dravidian languages such as tamil and telugu. our investigation delves into emotion detection, building upon existing studies and laying a foundational framework for future directions in this domain. the insights gleaned from our research contribute to a growing body of knowledge, providing valuable groundwork for forthcoming explorations and advancements in understanding emotions in linguistic contexts. in the upcoming sections, we will explore the latest studies about emotion and stress identification in dravidian languages. we will share our approach, configuration, and methodology, followed by a presentation of the results and a concise analysis with future orientations. ## literature review india, renowned for its rich history and culture, is witnessing a growing prevalence of stress across its diverse population. from the young to the adults, stress permeates various aspects of life, encompassing academic pressures and professional challenges. in education, the pressure on students to achieve high standards has led to prolonged stress, impacting mental health and, at times, resulting in severe consequences, as noted by " @xcite in their research study. research by @xcite showcases the role of societal expectations and family pressures in exacerbating student stress. the advent of social media platforms has provided an avenue for individuals to express their emotions and state of mind. zaydman (2017) fetched 2.3 million mental health-relevant tweets, shedding light on stress and suicide tendencies(corpora in english). in response to this societal landscape, research on stress identification in textual corpora has flourished. @xcite explored stress detection in social interactions, achieving high accuracy using models like bert and random forest. similarly, @xcite al. (2023) through our experiment, we aspire to provide the community with a dependable method for predicting stress in tamil and telugu. we aim to establish a foundational benchmark for subsequent research endeavors in the field. ## task description the primary goal of this shared task is to develop a system capable of discerning between textual corpora that exhibit signs of stress and those that do not in dravidian languages, specifically tamil and telugu. stress, a multifaceted emotion stemming from various life factors, often prompts individuals to share their feelings online. constructing such a system holds the promise of aiding and supporting individuals displaying stress characteristics on social media, ultimately contributing to the reduction of depression rates within a broad community. despite its noble objectives, this shared task poses unique challenges. addressing the abstract concept of emotions, particularly stress, is inherently complex. the task's focus on tamil and telugu languages introduces additional difficulties, such as the limited resources available for process-ing and the lack of standardization observed in these languages. decrypting the nuances of emotional variations in these languages amplifies the complexity of the problem. however, the absence of tonal characteristics shared by languages like mandarin chinese renders the task more approachable. this undertaking not only underscores the significance of emotional analysis but also holds the potential to make a meaningful impact on mental health outcomes, emphasizing the importance of computational linguistics in addressing real-world challenges. ## approach to address the challenge at hand, we systematically evaluated various options to arrive at a logical and explainable solution. our decision-making process involved a thorough analysis of the syntactical structures in both languages, leveraging the support of the indicnlp project from @xcite . upon examining the indicnlp library tokenization process and our dataset, we concluded that our corpora required no further preprocessing to align with our objectives. during the feature extraction stage, we segmented the corpus into sentences and employed term-frequency inverse document frequency (tf-idf) as the sole feature type, alongside consideration for bigrams. these choices were made with a focus on their compatibility with the dataset characteristics and the task requirements. ## experimental setup to conduct our experiments, we relied primarily on the scikit-learn @xcite , and fasttext packages due to their versatility and effectiveness in implementing various machine learning models. scikit-learn provided a robust set of tools for traditional machine learning models, while fasttext, with its efficient text classification capabilities, complemented our exploration. concerning the hardware, our experiments were executed on a computer running ubuntu 22.04, equipped with 64 gib of random access memory (ram), and powered by an amd ryzen 7000 series 7 processor running at 3.3 ghz. this configuration was chosen for its capability to handle the computational demands of traditional machine learning models, even with a limited amount of data. the selected hardware configuration proved adequate for our experimental goals, ensuring efficient execution and allowing us to gain meaningful insights into stress identification in dravidian languages. ## results we present the performance metrics of our stress identification model for tamil and telugu languages. the evaluation metrics include accuracy, macro f1-score, macro-recall, and weighted precision. ## discussions and limitations our model for the telugu language exhibited commendable performance by securing the second rank in the shared tasks. we can observe that both our models did not overfit and remained stable on the test set. this achievement underscores the effectiveness of the chosen approach and the potential for accurate stress identification in dravidian languages. it is noteworthy that sufficient data and additional features related to the two languages tamil and telugu can help our models become more reliable in the identification of emotional stress in textual corpora in both tamil and telugu. this observation emphasizes the importance of data abundance in enhancing model performance, paving the way for more accurate and robust stress identification systems. however, it's essential to acknowledge certain limitations in our current approach. one notable limitation is we did not make use of phonology or phonetic features which are important factors in extracting meaningful information from tamil or telugu. despite the capabilities of indicnlp for phonetic feature extraction, this aspect was not explored in our experiments. future investigations could delve into extracting phonetic features, potentially enriching the model's understanding of stress patterns in the spoken language. moreover, there exists ample room for further experiments. techniques such as embedding learning offer a promising avenue for feature extraction, potentially capturing nuanced linguistic representations. alternatively, leveraging large language models can provide a comprehensive understanding of stress-related features in dravidian languages since both languages make use of context. ## conclusion in conclusion, while the success of our model in the telugu language showcases the effectiveness of our approach, ongoing research, and experimentation are crucial to unlocking the full potential of stress identification in tamil and telugu. exploring additional features, incorporating advanced techniques, and leveraging large-scale language models are avenues for future research, promising advancements in computational linguistics for emotion analysis in diverse linguistic contexts.