Opinion Mining Using Twitter Feeds for Political Analysis

Sentiment analysis deals with identifying and understanding opinions and sentiments expressed in a particular text. The masses give their opinion regarding various subjects on social media platforms using tweets, status updates and blogs. By analyzing this very data, we can gain better insight of the public opinion on any subject in specific. On performing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters allowed in Twitter is 140. In this paper, we try to analyze the twitter posts about government issues and political reforms. The proposed framework uses Twitter as the platform to analyze the emotions of the users using Sentiment Analysis. The system will use the opinions of the users, analyze the reaction and then map it to the appropriate region

Identity Theft Prediction Using Game Theory

Digital devices have become an integral part of every person’s life. The range of use of these devices is increasing daily. Over the decades, the number of users has increased from thousands to millions and is still increasing. Due to the multi-functional features of digital devices, their importance is now being recognized more than ever. Initially, they were used only for calling and texting; however, nowadays, they are also being used to store relevant data such as account numbers, card numbers, credentials, private pictures, passport copies, etc. The most common form of Identity Theft attack is through stealing passwords. Once the password is stolen, user privacy is lost, and the data is compromised. Thus, a system consisting of a database that comprises of leaked passwords collected from various social sites and common passwords as a part of a dictionary attack used by hackers has been created by us. When a user enters his/her password, it runs it through the database and checks for a match. This document emphasizes on how game theory can be utilized in predicting the possibility of a successful attack and discusses essential concepts such as the various components of game theory and Nash Equilibrium

An ML and SMS remote access based model for Anti-theft protection of Android devices

Android phones being stolen is a significant problem that causes concerns to intellectual privacy and property. Always protecting smartphones from being stolen is a problem that remains. The key findings of the survey of existing systems for theft protection are, they provide various efficient functionalities but fail when the internet is unavailable or require specialized equipment to detect thefts. Most of these solutions are not free of charge, inefficient, time-consuming, or/and inflexible. This paper puts forward a system that provides an ML-based real-time anti-theft and remote access system for android devices. It detects theft using SVM-RBF model trained on feature-set extracted from the inertial sensor’s data with an accuracy of 0.76. Whereas remote access is provided using short message services (SMS). The salient feature of this system is minimal configuration without intruding human-assisted tasks. Moreover, it will be an excellent help for authentic smartphone users to realize the theft situation and utilize the remote access features