Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression
| Autor: | Anees Ul Hassan, Muhammad Sadiq, Musarrat Hussain, Jamil Hussain, Sungyoung Lee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
education.field_of_study
Social network business.industry Computer science media_common.quotation_subject Population Sentiment analysis 02 engineering and technology Machine learning computer.software_genre Feeling 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Artificial intelligence business education computer media_common |
| Zdroj: | ICTC |
| DOI: | 10.1109/ictc.2017.8190959 |
| Popis: | The advent of different social networking sites has enabled anyone to easily create, express, and share their ideas, thoughts, opinions, and feelings about anything with millions of other people around the world. With the advancement of technology, mini computers and smartphones have come to human pockets and now it is very easy to share your idea about anything on social media platforms like Facebook, twitter, Wikipedia, LinkedIn, Google+, Instagram etc. Due to the tremendous growth in population and communication technologies during the last decade, use of social networks is on the rise and they are being used for many different purposes. One such service for which their use may be explored is an analysis of users post to diagnosis depression. In this paper, we present how to find the depression level of a person by observing and extracting emotions from the text, using emotion theories, machine learning techniques, and natural language processing techniques on different social media platforms. |
| Databáze: | OpenAIRE |
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