Deep Learning Based Approach For Detecting Suicidal Ideation in H indi- E nglish Code-Mixed Text: Baseline and Corpus (2021)

Abstract: Suicide rates are rising among the youth, and the high association with suicidal ideation expression on social media necessitates further research into models for detecting suicidal ideation in text, such as tweets, to enable mitigation. Existing research has proven the feasibility of detecting suicidal ideation on social media in a particular language. However, studies have shown that bilingual and multilingual speakers tend to use code-mixed text on social media making the detection of suicidal ideation on code-mixed data crucial, even more so with the increasing number of bilingual and multilingual speakers. In this study we create a code-mixed Hindi-English (Hinglish) dataset for detection of suicidal ideation and evaluate the performance of traditional classifiers, deep learning architectures, and transformers on it. Among the tested classifier architectures, Indic BERT gave the best results with an accuracy of 98.54%.

## introduction a study by the world health organization (who), has found that nearly 700,000 people die of suicide each year (who). suicidal ideation, the act of thinking about, considering or planning suicide, can be attributed to multiple reasons including mental illness, traumatic stress, loss or fear of loss, social isolation, biological factors, environmental factors, genetic factors and situational factors @xcite . in the wake of coronavirus @xcite , an increasing number of individuals across the world have become victims to one or more than one of these factors, that has led to increased rates of suicide worldwide @xcite . it has been found that increase in consumption and posting on social media has a direct correlation to the tendency of expressing desires, thoughts, and intentions on pro-suicide platforms before attempting suicide @xcite . with covid-19 driving social media consumption up by 72% and posting by 43% such incidents recorded an all-time high @xcite . @xcite mentioned that more than half of the people on social media platforms are not native english speakers and @xcite confirmed that about 50% of the posts on twitter are in languages other than english. these studies substantiate the need of a much broader scope for detection of suicidal ideation on social media than just the english language. @xcite found that over 26% of the indian population speaks more than one language, often in the form of code-switching or code-mixing. code-switching occurs when an individual alternates between multiple languages in the context of a single conversation or situation while codemixing is the use of two or more languages by an individual below clause level in a single social context. however, working with code-mixed data presents it's own set of challenges, including the creation of a large number of new constructions for understanding the syntax and semantics of the two or more combined languages, the availability of very small amounts of annotated data, and the use of drastically different approaches when compared to monolingual data @xcite . in this paper, we aim to detect suicidal ideation in code-mixed hinglish. although significant work is available for suicidal ideation detection in english @xcite al. (2020), coppersmith et al. (2018), mbarek et al. (2019), ophir et al. (2020) , ramírez-cifuentes et al. (2020), sawhney et al. (2020), shaoxiong ji (2020), tadesse et al. (2019), vioules et al. (2018)), detection of suicidal ideation in code-mixed languages is relatively unexplored. to the best of our knowledge, we are the first to identify suicidal ideation in code-mixed ## methodology the proposed methodology consists of three major parts, each consisting of a major contribution of our work. ## experimental settings / modeling for training our deep learning models, we made a fifteen percent validation split for a total of 20 epochs while the transformers are trained for 5 epochs on the same split. the model checkpoints are saved at each epoch and the model with highest validation accuracy and lowest difference from training accuracy is saved as the final model to ensure prevention of overfitting. word embeddings are trained using negative sampling polarity, an embedding size of 300, a window length of 10. the adam optimizer is employed in all of the models, coupled with the binary cross entropy loss function. with the exception of the output layer, which has sigmoid activation, all layers have relu activation. we tested cnn models with various kernel sizes, number of kernels, dropouts, and strides to see how well they performed. with the following parameters, the best results are obtained: @xmath0, number of @xmath1, @xmath2.5 for all rnns the hyperparameters used are dropout for recurrent @xmath3.25, dropout for input @xmath4.25, and number of lstm @xmath5. ## results we have tested our dataset on traditional machine learning classifiers, deep learning models and transformers. the results of well known traditional classifiers have been listed in given the code-mixed nature of the corpus, the transformers used for classification are mbert and indicbert. the problem of detecting suicidal thoughts on code-mixed hindi-english data is compounded by a lack of clean data and linguistic complications connected with code-mixed data. more data, as well as well labelled classes, are necessary to allow the model to accept noise in textual input, spelling errors, diverse contexts, and stemming words. ## conclusions and future work the current research is the first attempt to investigate multilingual text classification to predict suicidal ideation in code-mixed hindi-english texts, and proposes a baseline for further work along with a corpus for validation. for the objective of detecting suicidal text on the created corpus, several deep learning based models are used, including cnn, lstm, bi-directional lstm, attention based bi-directional lstm, mbert and indic bert.since texts containing suicidal ideation in hinglish are not available directly, a dataset is created by using a semi-supervised approach to generate code-mixed hinglish text using pre-trained encoders and transfer learning from anonymized data in english from reddit. a comparison of the various models indicated that both bert-based models mbert and indic bert give exceptional results and have accomplished the task with over 96% accuracy. multilingual text classification is still a developing field, and future advancements could lead to better outcomes. comparing vectors aligned with multilingual word embeddings generated using muse to fasttext pre-aligned word embeddings may generate better results. working on factors such as sarcasm, humor and irony that affect the classification of suicidal ideation explicitly, along with their inclusion in the creation of the model could be another potential avenue for future research.