Sentiment Analysis on YouTube: A Brief Survey <s> Predicting YouTube Comments <s> This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Predicting YouTube Comments <s> 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. <s> BIB002 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Predicting YouTube Comments <s> 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. <s> BIB003 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Predicting YouTube Comments <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> BIB004 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Predicting YouTube Comments <s> 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. <s> BIB005
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. 