Probabilistic neural network and word embedding for sentiment analysis

5Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

In the present days, Artificial Intelligence (AI) is an attractive area of research along with numerous practicable purposes and vigorous subject matters and tasks, such as, understand speech, natural language, diagnose medicine and support basic research. In this study deep learning (DL) techniques, i.e. Probabilistic Neural Network (PNN) and Word Embedding (WE) will be used for sentiment analysis. The entire proposed framework will be divided into three phases: (a) normalization, (b) word vectorization, and (c) execution of proposed model.

References Powered by Scopus

Probabilistic neural networks

3278Citations
N/AReaders
Get full text

New types of deep neural network learning for speech recognition and related applications: An overview

1002Citations
N/AReaders
Get full text

A leaf recognition algorithm for plant classification using probabilistic neural network

749Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Long short term memory (LSTM) model for sentiment analysis in social data for e-commerce products reviews in Hindi languages

40Citations
N/AReaders
Get full text

A novel approach for sentiment analysis using deep recurrent networks and sequence modeling

3Citations
N/AReaders
Get full text

The Implementation of Probabilistic Neural Networks to Sentiment Analysis of National Principle and Religion Issues in Indonesia

2Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Alam, S., & Yao, N. (2018). Probabilistic neural network and word embedding for sentiment analysis. International Journal of Advanced Computer Science and Applications, 9(7), 48–53. https://doi.org/10.14569/IJACSA.2018.090708

Readers over time

‘18‘19‘20‘21‘23‘24‘2502468

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 5

63%

Lecturer / Post doc 2

25%

Professor / Associate Prof. 1

13%

Readers' Discipline

Tooltip

Computer Science 5

63%

Engineering 2

25%

Business, Management and Accounting 1

13%

Save time finding and organizing research with Mendeley

Sign up for free
0