Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.
It is important to capture performance trade-offs of various design options early in a design process. The system architects must get reliable performance projections for a design with very little detail. This information must be updated with greater accuracy as more details of the design are determined. There is an increasing demand for architectural tools which assist in making rapid decisions between performance and cost. We have experimented with modeling and simulating a rather complex computer system using such a tool. After putting together a list of requirements for the desired software tool, we explored the existing platforms. The selected package had the most coverage of our stipulated requirements. After choosing our platform, we modeled our system and ran some test inputs. The results were quite encouraging, especially given our short design cycle. This paper discusses our experience in modeling and simulating a rather complex system. We describe the requirements of our system level simulation platform, present an example of what is available, and discuss the available and desirable features.
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