Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
subject: Deep learning; Natural Language Processing; Polarity Identification; Sentiment Analysis; Time-series data; Weight priority
description: Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE.
creator: Yeo I.; Balachandran K.
source: 2019 International Conference on Data Science and Communication, IconDSC 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2019-01-01
rights: Restricted Access
relation: ISBN: 978-153869319-3
format: Online
language: English
type: Conference paper
coverage: Yeo I., Dept. of Computer Science and Engineering, CHRIST, Deemed to be University, Bangalore, India; Balachandran K., Dept. of Computer Science and Engineering, CHRIST, Deemed to be University, Bangalore, India