Sentiment Analysis on YouTube: A Brief Survey <s> Event Classification <s> For the Affective Text task at SemEval-2007, University Paris 7's system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and a subset of WordNet-Affect). We use a parser to find the head word, considering that it has a major importance. We also detect contrasts (between positive and negative words) that shift valence. Our knowledge-based system achieves high accuracy on emotion and valence annotation. These results show that working with linguistic techniques and a broad-coverage lexicon is a viable approach to sentiment analysis of headlines. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Event Classification <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> Event Classification <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> BIB003
To categorize the events as critical, high, medium, low or noise is known as Event Classification. Events are classified as Standard or Special Events. A technique i.e. a text processing pipeline is used to find out a collection for categories of video event automatically. For this, video titles and description are analyzed and then WordNet is applied so that category may be selected  . Users can upload their own videos to YOUTUBE. There is a title and description of each video and this title and description have some information about that video. But this information provided by title and description is not sufficient to understand the video contents BIB003 BIB002 BIB001 . So this information is called weak label information. To make it useful information, first, structure of language used in titles and description must be clear. Secondly a method should be developed to filter categories so that relevant categories may be retained. In order to solve 