Sentiment Analysis on YouTube: A Brief Survey <s> Negation 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> Negation Detection <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> BIB002
Negation is an operation used in grammar. A statement is negated by replacing it with opposite statement [18] . A negative form of statement describes the falsity of the basic statement. Negation of Verb Phrases: Sentences can first be negated through negation of verb phrase [18] . For this negation, negative adverb not is used. For example; (i) They have books. (Positive) (ii) They do not have books (Negated). Negation of Noun Phrases: Sentences can secondly be negated through noun phrase negation [18] . Noun phrases can be negated by using the quantifying determiner no in front of the noun phrase. For example; (i) They have money. (Positive) (ii)They have no money (Negated). Negating Adjective Phrases: Adjective phrases are negated by using the negative adverb "not" in front of the adjective phrase [18] . For example (i)The book is heavy (Simple sentence) (ii) The book is not heavy(Negated). Other Quantifiers used for Negation: Sentences can also be converted into negative form by using different quantifiers, such as, no one, nobody, and none, never, nowhere, nothing, hardly, a scarcely [18] . The lexicon consisting of negation phrases having a list of high-frequency terms in a negative context is maintained and then phrase level polarity score calculation is performed in such a way that if the input is identified as positive phrase then previously determined sentiment score is recovered from negative to positive  BIB001 BIB002 .