Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter
نویسندگان
چکیده
منابع مشابه
MLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...
متن کاملContextualized Sarcasm Detection on Twitter
Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance ...
متن کاملTweet Sarcasm: Mechanism of Sarcasm Detection in Twitter
The www is growing at an alarming rate . Users have started participating actively on Internet by giving their opinions on products, services and blogs. Study and analysis of such opinions is known as Opinion Mining. But sometimes users prefer being sarcastic. Sarcasm is a linguistic phenomenon in which people state the opposite of what they actually mean. Sarcasm Detection is a challenging tas...
متن کاملEnsemble-based multi-filter feature selection method for DDoS detection in cloud computing
Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud D...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2020
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2020/2860479