Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is productbased sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features.Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain.

Abstractn today's world, Social Networking website like Twitter, Facebook , Tumbler, etc. plays a very significant role. Twitter is a micro-blogging platform which provides a tremendous amount of data which can be used for various application of sentiment Analysis like predictions, review, elections, marketing, etc Sentiment Analysis is a process of extracting information from large amount of data, and classifies them into different classes called sentiments. Python is simple yet powerful, high-level, interpreted and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provide graphical demonstration for representing various results or trends and it also provide sample data to train and test various classifier respectively. Sentiment classification aims to automatically predict sentiment polarity of users publishing sentiment data. Although traditional classification algorithm can be used to train sentiment classifiers from manually labelled text data, the labelling work can be time-consuming and expensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the difference between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in target domain but have some labelled data in a different domain, regarded as source domain.

This paper reports research into representing edge detectors,

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Face recognition has an important application in criminal investigation. Previous research on sketch recognition focused on matching sketches drawn by professional artists. There has been number of representation methods are used to solve the problem of matching facial sketches to photographs.In the proposed system composite sketches are synthesized using one of the several facial composite software systems. A component-based representation (CBR) approach is used to measure the similarity between a composite sketch and mugshot photograph. First detect the facial landmarks in composite sketches and face photos using an active shape model (ASM) followed by computing length between elements. Features are then extracted for each facial component using multiscale local binary patterns (MLBPs), and per component similarity are calculated. In proposed system per component features are measured and compared with the features of the mugshot gallery set for matching. Depending on the component matching images will be display in sorted order.