Natural Language Processing and Lexical Approach for Depression Symptoms Screening of Indonesian Twitter User

Abstract

The shame and disgrace of depression cause people not to seek help for the problem. Contemporary social media technologies such as Twitter provide an opportunity for people to be able to express their feelings in an anonymous and confidential environment. In this study symptoms lexicon terms are used with the frequency lexicon for weighting and perform depression score calculations from text materials that have undergone Natural Language Processing. Through the scraping process, 55 relevant users were found who netted depression-related keywords. For each user, a data search is made for a week before and after the initial tweet. Tweet extensions generate a total of 6055 tweets from 55 users in question. The sum of the scores becomes the label determinant of user depression, which is then compared to the labels that have been given manually by psychologists based on clinical screening standards. Based on comparative and evaluation results, the same F1 score of 0.47 is obtained for standard text processing and text-specific processing for Twitter, and a Sensitivity value of 0.89 at the threshold value of 0.5. A slightly better F1 Score value of 0.50 is obtained by text-specific processing on the threshold value of 0.8. Research shows differences in minor results between standard text processing and text-specific processing of Twitter. Seen some of the advantages of text-specific Twitter processing when handling non-standard text, thereby enhancing the fmdings of the Part-of-Speech tagging process.