
The Performance Differences between Using Recurrent Neural Networks and Feedforward Neural Network in Sentiment Analysis Problem
Author(s) -
Samar K. Abaas,
Loay E. George
Publication year - 2020
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2020.61.6.31
Subject(s) - computer science , artificial neural network , sentiment analysis , set (abstract data type) , entertainment , feedforward neural network , artificial intelligence , recurrent neural network , machine learning , the internet , field (mathematics) , natural language processing , world wide web , art , mathematics , pure mathematics , visual arts , programming language
With the spread use of internet, especially the web of social media, an unusual quantity of information is found that includes a number of study fields such as psychology, entertainment, sociology, business, news, politics, and other cultural fields of nations. Data mining methodologies that deal with social media allows producing enjoyable scene on the human behaviour and interaction. This paper demonstrates the application and precision of sentiment analysis using traditional feedforward and two of recurrent neural networks (gated recurrent unit (GRU) and long short term memory (LSTM)) to find the differences between them. In order to test the system’s performance, a set of tests is applied on two public datasets. The first dataset is collected data from IMDB that contains movie reviews expressed through long sentences of English, whereas the second dataset is a collection of keyword search results of tweets using the Twitter Search API; these tweets are written in English words with short sentences. In this work, a certain pre-processing operation is added to the system and a set of tests is conducted to evaluate the performance enhancement on the whole system due to the addition of these operations. The results of the usage of the traditional feedforward neural networks are poor and do not perform the desired purpose in analysis, because of their inability to save information at a long term and, therefore, their loss of efficiency. While the results of using GRU and LSTM are relatively good and do perform the desired purpose in analysis. A recurrent neural network has been built so that any type of text-related data can be pushed to get the polarity of sentiment by multi deep operations that are dependent on the extracted information.