Simulation and Analysis of III V Characteristic and Bandgap Design for Heterojunction Laser Diode ijtsrd
This paper presents a system that extracts information from automatically annotated tweets using well known existing opinion lexicons and supervised machine learning approach. In this paper, the sentiment features are primarily extracted from novel high coverage tweet specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment word hashtags and from tweets with emoticons. The sentence level or tweet level classification is done based on these word level sentiment features by using Sequential Minimal Optimization SMO classifier. SemEval 2013 Twitter sentiment dataset is applied in this work. The ablation experiments show that this system gains in F Score of up to 6.8 absolute percentage points.
BY Nang Noon Kham “Lexicon Based Emotion Analysis on Twitter Data”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,
Simulation and Analysis of III V Characteristic and Bandgap Design for Heterojunction Laser Diode ijtsrd IEEE PAPER
Multiscale Modeling Approach for Prediction the Elastic Modulus of Percolated Cellulose Nanocrystal CNC Network ijtsrd
Lexicon Based Emotion Analysis on Twitter Data ijtsrd