New forms of communication, such as online review sites, microblogging or personal blogs, and text messaging, have emerged and become ubiquitous over the past decade. While there is no limit to the range of information conveyed by tweets and texts, these short messages are often used to share people's opinions about what is happening in the world around them. With the growing availability and popularity of opinion-rich resources, new opportunities and challenges emerge as people can now actively use information technologies to understand the opinions of others. In this survey, we cover some techniques and approaches that promise to directly enable opinion-oriented information search systems. The main focus of the investigation is based on methods that address the new challenges posed by sentiment aware applications which are compared with those already present in the more traditional fact-based analysis. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayThis survey also includes material based on the synthesis of evaluative texts and on broader issues regarding privacy, manipulation and the economic impact that the development of opinion give rise to oriented information access services. To make our future work less difficult, a discussion of available resources and evaluation campaigns is also provided. In this project, we will find suitable sentiment analysis algorithms for political blog data and generate comparative experimental results with at least two different algorithms. Our primary motivation for working on this topic is that informal text genres present challenges for natural language processing that go beyond those typically encountered when working with more traditional text genres, such as newswire data. Tweets and texts are short: a sentence or headline rather than a document. The language used is very informal, with creative spelling and punctuation, misspellings, slang, new words, URLs and genre-specific terminology and abbreviations, such as RT for ""re-tweet"" and hashtag #, which are a type of tags for Twitter messages. To manage these challenges, we had to automatically extract and understand the opinions and feelings that people communicate. This has been the subject of research a lot recently. Another aspect of social media data such as Twitter messages is that they include rich structured information about the individuals involved in the communication. For example, Twitter maintains information about who follows whom, and retweets and tags within tweets provide information about the discourse. Modeling such structured information is important because: it can lead to more accurate tools for extracting semantic information and provides means to empirically study the properties of social interactions (for example, we can study the properties of persuasive language or which properties are associated with influential users).).