Sentiment Analysis on YouTube: A Brief Survey <s> Lexicon based detection <s> This paper introduces a methodology for determining polarity of text within a multilingual framework. The method leverages on lexical resources for sentiment analysis available in English (SentiWordNet). First, a document in a different language than English is translated into English using standard translation software. Then, the translated document is classified according to its sentiment into one of the classes "positive" and "negative". For sentiment classification, a document is searched for sentiment bearing words like adjectives. By means of SentiWordNet, scores for positivity and negativity are determined for these words. An interpretation of the scores then leads to the document polarity. The method is tested for German movie reviews selected from Amazon and is compared to a statistical polarity classifier based on n-grams. The results show that working with standard technology and existing sentiment analysis approaches is a viable approach to sentiment analysis within a multilingual framework. <s> BIB001 </s> Sentiment Analysis on YouTube: A Brief Survey <s> Lexicon based detection <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
Lexicon oriented techniques for the detection of sentiment polarity are based on different lexicons, such as, WordNet and SentiWordNet (SWN)    . For the detection of sentiments from user comments, an existing WordNet dictionary and a specific list are used. The specific list contains terms and phrases. These terms and phrases express user opinions   .The SentiWordNet (SWN) is a document resource which contains a list of English terms which have been attributed a score of positivity and negativity [14] .The SWN assigns polarity to each term and phrase of WordNet  15] .For analyzing user sentiment in comments, SentiWordNet   BIB001 ] is used. Through sentiment analysis positive and negative opinions, emotions can be identified. User comments can be analyzed by using SWN and customized social media specific phrase list  17] . When a user enters a comment, the system detects the number of those words having sentiment. A sentiment score is calculated by the number of entries for every word present in the list. If there is a single entry of a word then that word is assigned a polarity with the highest score. If there are more than one entries of a term, the scores of each class are averaged for normalization. For example, there are four scores for a term in a sentence. To find the sentiment polarity of the entire comment, the average score of all terms in the comment is calculated and the term having highest frequency is assigned either positive or negative polarity. Table 1 shows sentiment scores for terms  BIB002  BIB001 . 