scholarly journals Content Filtering of Social Media Sites Using Machine Learning Techniques

2021 ◽  
Author(s):  
U.S. Tambe ◽  
N.R. Kakad ◽  
S.J. Suryawanshi ◽  
S.S. Bhamre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Relations ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Content Filtering ◽  
Learning Techniques ◽  
Personal Interests

To build a social network or social relations between people, we use social networking platforms like Facebook, Twitter, apps, etc. Using this media, users can share their views and opinions about a particular thing. Many people use their media for personal interests, entertainment, the market stocks, or business purposes. Nowadays, user security is the major concern for social networking sites. Online social networks give a little bit of support regarding content filtering. In this article, we proposed a system that provides security regarding malicious content that is posted on their social networking sites. To filter the content that might be unwanted messages, labeled images, or vulgar images, we proposed three level architecture. The user can use the auto-blocking facility as well.

scholarly journals Prediction of Fake Tweets Using Machine Learning Algorithms

2021 ◽  
Author(s):  
M. Sreedevi ◽  
G. Vijay Kumar ◽  
K. Kiran Kumar ◽  
B. Aruna ◽  
Arvind Yadav
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Internet Media ◽  
Learning Techniques ◽  
False News

Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.

scholarly journals Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media Conversation

2021 ◽  
Vol 179 ◽  
pp. 821-828
Author(s):  
Andry Chowanda ◽  
Rhio Sutoyo ◽  
Meiliana ◽  
Sansiri Tanachutiwat
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Learning Techniques

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

scholarly journals Detection of Mentally Distressed Social Media Profiles Using Machine Learning Techniques

2020 ◽  
Vol 981 ◽  
pp. 022056
Author(s):  
T. Sravanthi ◽  
V. Hema ◽  
S Tharun Reddy ◽  
K Mahender ◽  
S Venkateshwarlu
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document