TY - GEN
T1 - A Multiclass Depression Detection in Social Media Based on Sentiment Analysis
AU - Mustafa, Raza Ul
AU - Ashraf, Noman
AU - Ahmed, Fahad Shabbir
AU - Ferzund, Javed
AU - Shahzad, Basit
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Deep learning
KW - Depression
KW - Machine learning
KW - Neural network
KW - Social media
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85085736543&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-43020-7_89
DO - 10.1007/978-3-030-43020-7_89
M3 - Contribución a la conferencia
SN - 9783030430191
T3 - Advances in Intelligent Systems and Computing
SP - 659
EP - 662
BT - 17th International Conference on Information Technology–New Generations, ITNG 2020
A2 - Latifi, Shahram
PB - Springer
T2 - 17th International Conference on Information Technology: New Generations, ITNG 2020
Y2 - 5 April 2020 through 8 April 2020
ER -