You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Sentiment Analysis on YouTube: A Brief Survey 
Chapter Title:  Predicting YouTube Comments 
1. Abstract of Cited Paper (BIB001):  In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain. 
2. Abstract of Cited Paper (BIB002):  There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from ifferent countries. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  The increased online presence of jihadists has raised the possibility of individuals being radicalised via the Internet. To date, the study of violent radicalisation has focused on dedicated jihadist websites and forums. This may not be the ideal starting point for such research, as participants in these venues may be described as "already made-up minds". Crawling a global social networking platform, such as YouTube, on the other hand, has the potential to unearth content and interaction aimed at radicalisation of those with little or no apparent prior interest in violent jihadism. This research explores whether such an approach is indeed fruitful. We collected a large dataset from a group within YouTube that we identified as potentially having a radicalising agenda. We analysed this data using social network analysis and sentiment analysis tools, examining the topics discussed and what the sentiment polarity (positive or negative) is towards these topics. In particular, we focus on gender differences in this group of users, suggesting most extreme and less tolerant views among female users.

Literature Review:
YouTube is a website for sharing of videos. Through YOUTUBE videos can be uploaded by the users. The users can also view, and share videos .YOUTUBE uses technologies of Adobe Flash Video and HTML5 in order to display a wide variety of user-generated videos [18] . YOUTUBE includes educational, music and short clips videos. Most videos allow users to leave comments. These user comments are analyzed in order to predict polarity BIB003  BIB002 . This aims at analyzing comments made on videos hosted on YouTube and predicting the ratings that users give to these comments. The ratings are basically number of people likes (positive rating) or dislikes (negative rating) BIB004 BIB001  . The authors refer to comments that have positive rating as accepted comments and those having negative ratings as unaccepted comments. When ratings of comments for videos are analyzed then it can provide indicators. By using these indicators high polarity of content can be achieved BIB004 BIB003 BIB005 . Using comment clustering and aggregation techniques, users of the system are provided with different views on that content. Further, different classifiers are built in order to estimate the ratings of comments. Through those classifiers comments can be structure and filtered automatically BIB004 BIB005 . To study online radicalization among YOUTUBE users, Bermingham et al. BIB005 analyzed user comments and the language which the users use for comments. In order to perform a high quality sentiment analysis, different techniques are proposed based on the method they use to select comments/words or sentences fromYOUTU.