Publication Type : Conference Paper
Publisher : IEEE
Source : 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Kanpur, India (2019)
Url : https://ieeexplore.ieee.org/document/8944758
Campus : Coimbatore
School : School of Dentistry
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Powerful weapon in today's world is ones emotion is social media. They have the power to make an individual trending overnight or even may pull down anyone reputation. In this paper, the sentiment analysis task has been performed by collecting the dataset from the publically available sources and by merging them together to form a new dataset for the sentiment analysis task that is positive or negative sentiment based on the context of the subject. A new reliable dataset is subjected to various pre-processing techniques and then the feature extraction techniques aftermath they are passed to the deep learning techniques out of which by using the text representation method, global vectors (glovec) with the long short-term memory (lstm) has the highest accuracy of 75%, which is the benchmark accuracy for this dataset. For the research purpose the dataset used in this paper is made available publically for research purpose.
Cite this Research Publication : K. S. Naveenkumar, Vinayakumar, R., and Dr. Soman K. P., “Amrita-CEN-SentiDB 1: Improved Twitter Dataset for Sentimental Analysis and Application of Deep learning”, in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019.