Category : M.Tech Thesis
Abstract. Steganography is a practice where some specific piece of information is kept hidden between the sender and receiver. The Information can be in any sort of forms such as an audio file, video file or some text message. The intended media where the information is required to be hidden can be some text file, audio file & video file. The main idea here is of hiding information within some other information in such a way that it is difficult or even impossible to find its existence in the information. Invisible inks, microdots, and many others are used by traditional methods. There are many different methods to use the concept of Steganography but the most popular one is digital images. The modern stenographic techniques make an attempt to exploit digital media images, video files, audio files, etc.
The sentiment is an attitude, thought or your verdict which comes from feelings. So, the sentimental analysis is the study of the user’s sentiments towards any certain entity, any organisation, and celebrity or regarding any product. Due to the rapid increase in digital technology, the internet has become the cheapest resource to anyone where they can easily put contents, blogs, posts, news etc. by using social media platforms like Facebook, Twitter, and LinkedIn.
Nowadays, people prefer electronic media and internet generated reviews for taking any sort of decisions. Sentimental Analysis helps to know about the real opinion of different people regarding specific product, company, movies, news, any object and their attributes. It includes number of different branches like natural language processing, machine learning, text mining etc. By using these techniques and models, data is classified in the form of news articles, blogs, tweets, online reviews etc. into the positive, negative or neutral sentiments. Facebook is a kind of micro-blogging social networking site consisting of billions of users in these days. we have used R studio environment that offers number of packages to analyse data, graphics designing etc. However, R language is mostly used for the statistical problems.
K-Nearest neighbour classifiers are defined by their characteristic of classifying unlabelled examples by assigning them the class of the most similar labelled examples. Despite the simplicity of this idea, nearest neighbour methods are extremely powerful. They have been used successfully for: • Computer vision applications, including optical character recognition and facial recognition in both still images and video • Predicting whether a person enjoys a movie which he/she has been recommended (as in the Netflix challenge) • Identifying patterns in genetic data, for use in detecting specific proteins or diseases
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