Over the past decade, new forms of communications, such as online review sites, microblogging or personal blogs and text messaging, have emerged and have become omnipresent. Although 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 increasing availability and popularity of opinion-rich resources, new opportunities and challenges arise as people can now actively use information technologies to understand the opinions of others. In this survey, we discuss some techniques and approaches that promise to directly enable opinion-oriented information retrieval systems. The main focus of the survey is based on methods that address new challenges raised by sentiment-aware applications, compared to those already present in more traditional fact-based analysis. Say no to plagiarism. Get a tailor-made essay on "Why violent video games should not be banned"? Get an original essay This investigation also includes material based on the summary of an assessment text and on broader issues regarding privacy, manipulation and economic impact that the development of opinion Service-oriented information access services give rise to. To facilitate our future work, a discussion of available resources and evaluation campaigns is also provided. In this project, we will propose sentiment analysis algorithms suitable for political blog data and generate comparative experimental results with at least two different algorithms. The main motivation for us to work on this topic is that informal text genres present challenges for natural language processing beyond those typically encountered when working with more traditional text genres, such as data press wires. Tweets and texts are short: a sentence or title rather than a document. The language used is very informal, with creative spelling and punctuation, misspellings, slang, new words, URLs, and gender-specific terminology and abbreviations, such as RT. for "re-tweet" and # hashtags, which are a type of tagging for Twitter messages. To address such challenges, we had to automatically tap into and understand the opinions and feelings people communicate. This has very recently been the subject of research. Another aspect of social media data such as Twitter messages is that it contains 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 precise tools for extracting semantic information, and It provides means to empirically study the properties of social interactions (for example, we can study the properties of persuasive language or properties associated with influential users).).