Twitter Sentiment Analysis Based on Neural Network Techniques

Title

Twitter Sentiment Analysis Based on Neural Network Techniques

Creator

Singal A.; Thiruthuvanathan M.M.

Description

Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 33-48.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Comparison of various classification techniques; Convolutional neural network (CNN); Decision tree; Multilayer perceptron (MLP); Nae Bayes; Recurrent neural network (RNN); SVM; Twitter sentiment analysis

Coverage

Singal A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India; Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-16-9416-5_3

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133695429&doi=10.1007%2f978-981-16-9416-5_3&partnerID=40&md5=65716e980f1bcc4391fd908037e65559