Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links...

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Veröffentlicht in:Computational intelligence and neuroscience 2022-04, Vol.2022, p.5211949-14
Hauptverfasser: Rodrigues, Anisha P, Fernandes, Roshan, A, Aakash, B, Abhishek, Shetty, Adarsh, K, Atul, Lakshmanna, Kuruva, Shafi, R. Mahammad
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container_issue
container_start_page 5211949
container_title Computational intelligence and neuroscience
container_volume 2022
creator Rodrigues, Anisha P
Fernandes, Roshan
A, Aakash
B, Abhishek
Shetty, Adarsh
K, Atul
Lakshmanna, Kuruva
Shafi, R. Mahammad
description In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.
doi_str_mv 10.1155/2022/5211949
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subjects Analysis
Artificial neural networks
Bayes Theorem
Bayesian analysis
Classifiers
Data mining
Datasets
Decision trees
Deep Learning
Digital media
Feature extraction
Humans
Learning algorithms
Long short-term memory
Machine Learning
Methods
Neural networks
Recurrent neural networks
Sentiment Analysis
Social Media
Social networks
Spam (Junk email)
Support vector machines
title Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques
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