Hate Classifier for Social Media Platform Using Tree LSTM

Social-media without a doubt is one of the most noteworthy developments ever. From associating with individuals across the globe for sharing of data and information in an infinitesimal of a second, online media stages have enormously altered the method of our lives. This is joined by a steadily expanding utilization of social media, less expensive cell phones, and the simplicity of web access which have additionally prepared for the huge development of social media. To place this into numbers, according to an ongoing report, billions of individuals all over the planet presently utilize web-based media every month, and a normal amount of almost 2 million people new clients are going along with them consistently. While web-based media stages have permitted us to interface with others and fortify connections in manners that were not conceivable previously. Unfortunately, they have additionally turned into the default gatherings for can’t stand discourse. Online hate is a wild issue, with the adverse result of disallowing client support in web-based conversations and causing mental mischief to people. Since hate is pervasive across friendly, media stages, our objective was to foster a classifier that is feasible to train classifiers that can identify hateful remarks with strong execution and with the portion of misleading up-sides and negatives staying inside sensible limits

A COMPARATIVE PERFORMANCE ANALYSIS OF REAL-TIME PRIORITY QUEUES By

I would like to express my deep appreciation to my advisor, Dr. Baker without whose constant support and encouragement, this project would not have seen completion. His original ideas, comments and suggestions were invaluable to me at all times. I also wish to thank my committee members, Dr. Lacher and Dr. Van Engelen for spending their time and effort reviewing this report. I am grateful to Mr. Gaitros, who has been very helpful to me by lending me the resources needed to complete this work. My sincerest gratitude goes to my friends, peers and colleagues, Bharath Allam, Nikhil Iyer, and Nikhil Patel, who were constantly with me with their suggestions and support. I wish to thank Smita Bhatia for her immense patience and inexhaustible support, and my parents, Shaila and Jayprakash for bearing with me through this considerable length of time Last but not the least, a special thanks goes to Douglas Adams for helping me understand the concept of the Babel fish