A Multiclass Depression Detection in Social Media Based on Sentiment Analysis

Raza Ul Mustafa, Noman Ashraf, Fahad Shabbir Ahmed, Javed Ferzund, Basit Shahzad, Alexander Gelbukh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

© Springer Nature Switzerland AG 2020. Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers.
Original languageAmerican English
Title of host publicationA Multiclass Depression Detection in Social Media Based on Sentiment Analysis
Pages659-662
Number of pages4
ISBN (Electronic)9783030430191
DOIs
StatePublished - 1 Jan 2020
EventAdvances in Intelligent Systems and Computing -
Duration: 1 Jan 2020 → …

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1134
ISSN (Print)2194-5357

Conference

ConferenceAdvances in Intelligent Systems and Computing
Period1/01/20 → …

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