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Sentiment Analysis From Depression-related User-generated Contents from Social Media

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dc.contributor.author Saha, Ananna
dc.contributor.author Marouf, Ahmed Al
dc.contributor.author Hossain, Rafayet
dc.date.accessioned 2022-04-04T03:57:43Z
dc.date.available 2022-04-04T03:57:43Z
dc.date.issued 2021-07-01
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7733
dc.description.abstract In this paper, we try to detect the sentiment levels such as positive, negative and neutral sentiments from depression related posts and comments generated in social media platforms. Social media platforms such as Facebook, Twitter are not only used for communication or building networks among connections, but also are getting useful for supporting needy peoples who are on special need or care in terms of mental support. In Facebook, there are several depression support groups, which are very much effective to provide mental support to the victims. In this paper, we try to formalize the depression-related posts and comments into a concise lexicon database and detect the sentiment levels form each instance. We have segmented the total work into two parts: sentiment detection and applying machine learning algorithms to analyze the ability to detect sentiment from such special category of texts. We have utilized python textblob package to detect the sentiment levels and applied traditional machine learning algorithms such as Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Sequential Minimal Optimization (SMO), Logistic Regression (LR), Adaboost (AB), Bagging (Bg), Stacking (St) and Multilayer Perceptron (MP) on the linguistic features. We have determined the precision, recall, F-measure, accuracy, ROC values for each of the classifiers. Among the classifiers Random Forest has outperformed others showing 60.54% correctly classified instance. We believe such sentiment analysis on special category of texts may lead to further investigation in natural language understandings. en_US
dc.language.iso en_US en_US
dc.publisher 2021 8th International Conference on Computer and Communication Engineering (ICCCE), IEEE en_US
dc.subject Support vector machines en_US
dc.subject Sentiment analysis en_US
dc.subject Machine learning algorithms en_US
dc.subject Social networking (online) en_US
dc.subject Blogs en_US
dc.subject User-generated content en_US
dc.subject Linguistics en_US
dc.title Sentiment Analysis From Depression-related User-generated Contents from Social Media en_US
dc.type Article en_US

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