Sentiment Analysis on YouTube: A Brief Survey <s> Introduction <s> Established in 2005, YouTube has become the most successful Internet site providing a new generation of short video sharing service. Today, YouTube alone comprises approximately 20% of all HTTP traffic, or nearly 10% of all traffic on the Internet. Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. In this paper, using traces crawled in a 3-month period, we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their active life span, ratings, and comments. The series of datasets also allows us to identify the growth trend of this fast evolving Internet site in various aspects, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching or peer-to-peer distribution schemes to efficiently deliver videos to end users. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Introduction <s> A new method for sentiment polarity analysis is presented. The method first assigns scores to a sentence using SentiWordNet and then uses heuristics to handle context dependent sentiment expressions. Instead of using score of all synsets of a word listed in SentiWordNet we use score of synsets of the same parts of speech only. Our method shows significant improvement on movie-review dataset over the baseline. <s> BIB002 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Introduction <s> An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments. <s> BIB003 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Introduction <s> Abstract: Rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions contain public generated content about products, services, policies and politics. People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users for features of particular product or service. Therefore product features or aspects have got significant role in sentiment analysis. In addition to sufficient work being performed in text analytics, feature extraction in sentiment analysis is now becoming an active area of research. This review paper discusses existing techniques and approaches for feature extraction in sentiment analysis and opinion mining. In this review we have adopted a systematic literature review process to identify areas well focused by researchers, least addressed areas are also highlighted giving an opportunity to researchers for further work. We have also tried to identify most and least commonly used feature selection techniques to find research gaps for future work. <s> BIB004
The popularity of social media is increasing rapidly because it is easy in use and simple to create and share images, video even from those users who are technically unaware of social media. There are many web platforms that are used to share non-textual content such as videos, images, animations that allow users to add comments for each item  . YouTube is probably the most popular of them, with millions of videos uploaded by its users and billions of comments for all of these videos. In social media especially in YOUTUBE, detection of sentiment polarity is a very challenging task due to some limitations in current sentiment dictionaries. In present dictionaries there are no proper sentiments of terms created by community. It is clear from the studies conducted by BIB001 BIB003 that the web traffic is 20% and Internet traffic is 10% of the total YOUTUBE traffic. There are many mechanisms of YOUTUBE for the judgment of opinions and views of users on a video. These mechanisms include voting, rating, favorites, sharing. Analysis of user comments is a source through which useful data may be achieved for many applications. These applications may include comment filtering, personal recommendation and user profiling. Different techniques are adopted for sentiment analysis of user comments and for this purpose sentiment lexicon called SentiWordNet is used BIB002 BIB004 . In this paper a brief survey is performed on "sentiment analysis using YOUTUBE" in order to find the polarity of user comments. The whole paper is organized as follows: In Section-2 Survey Framework of sentiment analysis is discussed. In section 3 Evaluation is described. Section 4 gives conclusion. The Survey framework (Fifure 1) of our paper covers three main issues, namely, event classification, detection of sentiment polarity, and predicting YouTube comments. The detailed discussion about each component of the framework is given in the following section 