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

Abstract

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.

An Interactive Nursing Knowledge System Based on Artificial Intelligence and Its Implications for Neonatal Care Management

“…However, in response to the state of foreign research related to this topic, I found through my research of information related to the teaching resource pathways in neonatal nursing knowledge that there are very user-friendly advanced technology use products designed specifically for neonates, as well as books, videos, websites, and domestic neonatal nursing dissemination knowledge that are taught in the same way. A search of the App Store for iPhone revealed a selection of software with English text and teaching content on baby care [17].…”

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

“…For bone identification, Al-masni et al employed the YOLO model. As can be seen, the YOLO approach can produce significantly better results in medical imaging [20,21].…”

Study on the Risk of Transmission of COVID-19 Based on Population Migration

“…Studies have been conducted wherein deep learning and machine learning models were tested using dataset collected from seven countries which were impacted severely by the disease. The results revealed the superiority of the hybrid deep learning models in predicting the disease efficiently and accurately in comparison to the other state of the art approaches [37][38][39]. To implement the propagation risk analysis based on population migration, classical ML algorithms such as decision tree algorithm, random forest algorithm, adaboost algorithm, GBT algorithm, ExtraTrees algorithm, CatBoost algorithm, K-nearest neighbor (KNN) algorithm, and LightGBM algorithm are used.…”