Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

A Cluster-based Undersampling Technique for Multiclass Skewed Datasets

Imbalanced data classification is a demanding issue in data mining and machine learning. Models that learn with imbalanced input generate feeble performance in the minority class. Resampling methods can handle this issue and balance the skewed dataset. Cluster-based Undersampling (CUS) and Near-Miss (NM) techniques are widely used in imbalanced learning. However, these methods suffer from some serious flaws. CUS averts the impact of the distance factor on instances over the majority class. Near-miss method discards the inter-class data within the majority of class elements. To overcome these flaws, this study has come up with an undersampling technique called Adaptive K-means Clustering Undersampling (AKCUS). The proposed technique blends the distance factor and clustering over the majority class. The performance of the proposed method was analyzed with the aid of an experimental study. Three multiminority datasets with different imbalance ratios were selected and the models were created using K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF) classifiers. The experimental results show that AKCUS can attain better efficacy than the benchmark methods over multiminority datasets with high imbalance ratios