Sentiment Analysis on YouTube: A Brief Survey <s> Evaluation <s> User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Evaluation <s> The online information explosion has created great challenges and opportunities for both information producers and consumers. Understanding customer's feelings, perceptions and satisfaction is a key performance indicator for running successful business. Sentiment analysis is the digital recognition of public opinions, feelings, emotions and attitudes. People express their views about products, events or services using social networking services. These reviewers excessively use Slangs and acronyms to express their views. Therefore, Slang's analysis is essential for sentiment recognition. This paper presents a framework for detection and scoring of Internet Slangs (DSIS) using SentiWordNet in conjunction with other lexical resources. The comparative results show that proposed system outperforms the existing systems. (Fazal Masud Kundi, Shakeel Ahmad, Aurangzeb Khan, Muhammad Zubair Asghar. Detection and Scoring of Internet Slangs for Sentiment Analysis Using SentiWordNet. Life Sci J 2014;11(9):66-72). (ISSN:1097-8135). http://www.lifesciencesite.com . 10 <s> BIB002
Precision and Recall are usually used to measure the accuracy of a Sentiment Analysis system. Precision or positive predictive value is a part of relevant retrieved instances [18], while recall or sensitivity is the fraction of retrieved relevant instances. So precision and recall are based on two facts, namely, relevant understanding and measure  BIB002 . For example, a video has to be analyzed containing 11 dogs and some cats. The program which is used for recognizing dogs in that video identifies only 7 dogs. If 4 dogs out of 7 identified dogs are identified correctly and remaining 3 in 7 identified dogs are actually cats, then the precision of the program is 4/7 and its recall is 4/11. Similarly if a search engine returns 20 pages and only 10 pages out of 20 are relevant and search engine fails to return 30 additional relevant pages then its precision is 10/20 = 1/2 and its recall is 10/40 =1/4 Classification is performed using support vector machine (SVM)  BIB001  . Then video comments are divided into two parts. One part is called test data, and other part as training data. Test data changes after each test. Each video is described by a vector. After test, SVM classifier is used to classify category for each test vector. Table 2 shows the detailed results for each category. 