Search Results for author: Hamed Jelodar

Found 6 papers, 1 papers with code

Semantic Knowledge Discovery and Discussion Mining of Incel Online Community: Topic modeling

no code implementations19 Apr 2021 Hamed Jelodar, Richard Frank

Discovering the semantic aspects in Incel forums are the main goal of this research; we apply Natural language processing techniques based on topic modeling to latent topic discovery and opinion mining of users from a popular online Incel discussion forum.

Opinion Mining Retrieval

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

no code implementations16 Jan 2021 Hamed Jelodar, Rita Orji, Stan Matwin, Swarna Weerasinghe, Oladapo Oyebode, Yongli Wang

Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection.

Medical Diagnosis

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

no code implementations24 Apr 2020 Hamed Jelodar, Yongli Wang, Rita Orji, Hucheng Huang

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other.

Decision Making Sentiment Analysis +1

Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences

no code implementations20 Dec 2018 Hamed Jelodar, Yongli Wang, Mahdi Rabbani, Ru-xin Zhao, SeyedValyAllah Ayobi, Peng Hu, Isma Masood

According to importance of the subject, in this paper we discover the trends of the topics and find relationship between LDA topics and Scholar-Context-documents.

Recommendation Systems

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey

1 code implementation12 Nov 2017 Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, Liang Zhao

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents.

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