Sentiment Analysis on YouTube: A Brief Survey <s> Social <s> Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Social <s> Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news text affect the markets in profound ways, impacting on volumes of trades, stock prices, volatility and even future firm earnings. This paper aims to explore a computable metric of positive or negative polarity in financial news text which is consistent with human judgments and can be used in a quantitative analysis of news sentiment impact on financial markets. Results from a preliminary evaluation are presented and discussed. <s> BIB002
Media Aware Phrase Detection (SMAPD) In Social Media Aware Phrase Detection (SMAPD) comments observed in social media interactions are scanned for terms or adjectives having no entry in the SentiWordNet set. If term or phrase is detected then it is given a polarity either positive or negative. A list is prepared by searching Social media specific terms and phrases. This list is called "Social Media Aware Phrase List". Two different sources are used for the preparation of SMAPL (Social Media Aware Phrase List) (1) Existing data (2) Flagged comments. Those flagged comments are selected which have sentiments with high intensity. There are four major categories of comments: (1) Self-promotion: These are the comments which are used for subscription or for watching videos. (2) Propaganda: These are the comments which have strong beliefs on topics like Religion and socialism. (3) Abusive comments: These are those comments which contain extremely racist comments. (4) Miscellaneous comments: These are the comments which cannot be classified into any categories because the content of these comments looks normal. Term sentiment identification is very difficult in a sentence. Only terms in a sentence are not sufficient for the identification of sentiment. For example, the word "pretty" having a positive sense is when paired with qualifier "not" i.e. "not pretty" the polarity of the phrase changes from positive to negative sense. In order to detect sentiments properly, it is very important to identify such contextual situation  BIB001 . Comments in Social media are scanned to detect terms or adjectives but these comments are not present BIB002 in SentiWordNet set. This set can further be improved and used for comment classification. A text with four terms is scanned weather these terms are present or absent in the list of SentiWordNet set. If these terms or phrases are detected then they are assigned polarity values i.e. 0, 1 or -1.