Research Article Linguistic Analysis of Hindi-English Mixed Tweets for Depression Detection Carmel Mary Belinda M J , 1 Ravikumar S , 1 Muhammad Arif , 2 Dhilip Kumar V , 1 Antony Kumar K , 1 and Arulkumaran G 3 1 Department of Computer Science & Engineering, Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology, Chennai, India 2 Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan 3 Department of Electrical and Computer Engineering, Bule Hora University, Bule Hora, Ethiopia Correspondence should be addressed to Arulkumaran G; erarulkumaran@gmail.com Received 31 January 2022; Revised 15 February 2022; Accepted 21 February 2022; Published 12 April 2022 Academic Editor: Naeem Jan Copyright © 2022 Carmel Mary Belinda M J et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. According to recent studies, young adults in India faced mental health issues due to closures of universities and loss of income, low self-esteem, distress, and reported symptoms of anxiety and/or depressive disorder (43%). is makes it a high time to come up with a solution. A new classifier proposed to find those individuals who might be having depression based on their tweets from the social media platform Twitter. e proposed model is based on linguistic analysis and text classification by calculating probability using the TF * IDF (term frequency-inverse document frequency). Indians tend to tweet predominantly using English, Hindi, or a mix of these two languages (colloquially known as Hinglish). In this proposed approach, data has been collected from Twitter and screened via passing them through a classifier built using the multinomial Naive Bayes algorithm and grid search, the latter being used for hyperparameter optimization. Each tweet is classified as depressed or not depressed. e entire architecture works over English and Hindi languages, which shall help in implementation globally and across multiple platforms and help in putting a stop to the ever-increasing depression rates in a methodical and automated manner. In the proposed model pipeline, composed techniques are used to get the better results, as 96.15% accuracy and 0.914 as the F1 score have been attained. 1. Introduction Recent studies by the World Health Organization (WHO) [1] have revealed that 56 million Indians suffer from de- pression and another 38 million Indians suffer from anxiety disorders, and only a fraction of them receive adequate treatment. Even though this disorder is highly treatable, only a fraction of those suffering receive treatment, due to the societal stigma associated with mental health. Diagnosis and subsequent treatment for depression are often delayed, imprecise, and/or missed entirely. e social media activity of individuals presents a revolutionary approach to transforming early depression intervention services, es- pecially for young adults [2, 3]. Many depressed individuals seldom choose not to discuss their mental health with their family and friends because the taboo surrounding de- pression is still high, especially in India. Such individuals, when they tweet, consciously and subconsciously use words that indicate their mental health. e advent of social media platforms has made it relatively easier to find these indi- viduals [4, 5]. Since it is nearly impossible to check the hints from the posts of each user across all platforms for a human being or even a team of them, automating the entire process becomes the need of the hour. One such approach accepted globally is sentiment analysis [6, 7]. It is a cross platform ML approach that can be implemented to filter out a particular user based on the pattern of their social media posts. Hindawi Journal of Mathematics Volume 2022, Article ID 3225920, 7 pages https://doi.org/10.1155/2022/3225920