| Abstrakt: |
last years the risk of dying from depression has increased, Depression creates suicidal thoughts for people that cause serious disabilities in daily life. Sentiment analysis (SA) is a topic has been on research for decades, which aim to find the nature of the text and classifies into positive, negative and neutral, for today progression of technology a lot of data is available for SA from text and image. This paper aim to apply pre-processing, feature extraction and classification methods on Twitter tweets for perform SA focusing on depression. Deep Belief Network (DBN) technique used for the classification of the text data obtained from depression dataset, where the tweets are classified as positive or negative. The results are presented using standard evaluation metrics precision, recall, f-score and accuracy, which results are 99.97%, 98.47%, 99.21% and 98.77 respectively. [ABSTRACT FROM AUTHOR] |
