Sentimental Analysis of Twitter Using Long Short-Term Memory and Gate Recurrent Unit

S. Suriya¹ , K. Sindhu Meena²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.1-6, Feb-2020

Online published on Feb 28, 2020

Copyright © S. Suriya, K. Sindhu Meena . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Sentimental analysis is the process of discovering and categorizing the opinions in the texts/reviews. It is to determine the attitude of the speaker and also to identify the topic, product whether it is positive, negative or neutral. It is used in the process on analysing the texts, computing linguistics and biometrics for identifying systematically extracting, quantifying the information. This paper aims to classify the reviews and apply the word embedding techniques and to find which is more suitable word embedding for LSTM and GRU.LSTM and GRU are the solution for the vanishing gradient problem. LSTM more suits for classifying, processing and predicting the time series data. It is composed of 3 states input, output and forget state. The main thing about GRU is it can train to keep a information from long ago without wasting it through time or remove irrelevant information to be predicted. GRU is composed of update and reset gates. The performance are evaluated by using the accuracy, precision, recall and f1score.The anaconda navigator is used as a tool and python is the language used.

Key-Words / Index Term :
LSTM, GRU, sentimental analysis, word embedding techniques

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