Modelling of The New Forms of Online Communications

In the past decade, new forms of communications, such as Online review sites, microblogging or personal blogs and text messaging have emerged and become ubiquitous. While there is no limit to the range of information conveyed by tweets and texts, often these short messages are used to share opinions that people have about what is going on in the world around them. With the growing availability and popularity of opinion-rich resources new opportunities and challenges arise as people now can actively use information technologies for understanding the opinions of others. In this survey, we cover some techniques and approaches that promise us to directly enable opinion-oriented information seeking systems. The main focus of the survey is based on the methods that address the new challenges raised by sentiment aware applications which are compared to those already present in the more traditional fact-based analysis.

This survey also includes material based on the summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To make less difficult in our future work, a discussion of available resources and evaluation campaigns is also provided. In this project, we will come up with suitable sentiment analysis algorithms for political blog data and generate comparative, experimental results with at least two di?erent algorithms. The main motivation for us to work on this topic is, informal text genres presents challenges for natural language processing beyond those typically encountered when working with more traditional text genres, such as newswire data. Tweets and texts are short: a sentence or a 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 # hashtags, which are a type of tagging for Twitter messages. How to handle such challenges, we had to automatically mine and understand the opinions and sentiments that people are communicating. This is very recently been the subject of research. Another aspect of social media data such as Twitter messages is that it includes rich structured information about the individuals involved in the communication. For example, Twitter maintains information of who follows whom and re-tweets and tags inside of tweets provide discourse information. Modeling such structured information is important because:

1. It can lead to more accurate tools for extracting semantic information, and
2. It provides means for empirically studying properties of social interactions(e.g., we can study properties of persuasive language or what properties are associated with influential users).