• Priyanka E
• SriHari B Sondur
• Sanjay D Hebbar
• Manoj Kumar S

In this, we set forward a clever methodology for distinguishing the online media posts that are demonstrative of sorrow with the assistance of Long Short-Term Memory and natural Language Processing system, word implanting, tokenization, and factorization to recognize the message that communicates sensation of despondency and its connected opinions. The methodology precisely predicts feeling in the text through Deep Learning, which eliminates bogus up-sides by thinking about the prompt setting of words. The information for this examination has been scratched from public discussions on Reddit-a well know web-based media site. Naming is made prior to breaking down the information, which permits posts about a typical subject to be assembled. Posts from various gatherings examining melancholy and self-hurt are taken as the positive class, while posts from different, arbitrary gatherings are taken as the negative class. Given the variety of the negative class, the dataset might be supposed to be delegate of a true situation. The model created has applications across a wide range of spaces, for example, sent via web-based media and correspondence gatherings regularly visited by youngsters to identify conceivable hurtful inclinations

Natural Language Processing, Machine Learning, Python, Long Short Term-Memory.

"DEPRESSION ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.f261-f264, March-2022, Available :http://www.jetir.org/papers/JETIR2203532.pdf

"DEPRESSION ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppf261-f264, March-2022, Available at : http://www.jetir.org/papers/JETIR2203532.pdf