## 1 Introduction  
Undoubtedly, the way that machines and humans search, retrieve and manage information changes at a high rate. The potential information resources have increased because of the advent of Web 2.0 technologies. User-generated content is available from a large pool of sources, such as online newspapers, blogs, e-commerce sites, social media, etc. The quantity of information is continuously increasing in every domain. Initially, it was online retailers, such as Amazon, that identified possible profits by exploiting users' opinions. However, nowadays, almost everyone has realized that quality of services, marketing and maximization of sales cannot be achieved without considering the textual content that is generated by Internet users. Therefore, the task of identifying relevant information from the vast amount of human communication information over the Internet is of utmost importance for robust sentiment analysis. In fact, the existence of opinion data has resulted in the development of Web Opinion Mining (WOM) [1], as a new concept in web intelligence. WOM focuses on extracting, analyzing and combining web data about user thoughts. The analysis of users' opinions is substantial because they provide the elements that determine how people feel about a topic of interest and know how it was received by the market. In general, traditional sentiment analysis mining techniques apply to social media content, as well; however, there are certain factors that make Web 2.0 data more complicated and difficult to parse. An interesting study about the identification of such factors was made by [PERSON] et al. [2], in which they exposed important features that pose certain difficulties to traditional approaches when dealing with social media streams. The key difference lies in the fact that users are not passive information consumers, but are also prolific content creators. Social media can be categorized on a diverse spectrum, based on the type of connection amongst users, how information is shared and how users interact with the media streams:  
* Interest-graph media [3], such as Twitter, encourage users to form connections with others, based on shared interests, regardless of whether they know the other person in real life. Connections do not always need to be reciprocated. Shared information comes in the form of a stream of messages in reverse chronological order. * Professional Networking Services (PNS), such as LinkedIn, aim to provide an introduction service in the context of work, where connecting to a person implies that you vouch for that person to a certain extent and would recommend them as a work contact for others. Typically, professional information is shared, and PNS tend to attract older professionals [4]. * Content sharing and discussion services, such as blogs, video sharing (e.g., YouTube, Vimeo), slide sharing (e.g., SlideShare) and user discussion forums (e.g., CNET). Blogs usually contain longer contributions. Readers might comment on these contributions, and some blog sites create a time stream of blog articles for followers to read. Many blog sites also advertise automatically new blog posts through their users' Facebook and Twitter accounts. The following challenging features are also recognized by researchers and classified as openings for the development of new semantic technology and text mining approaches, which will be better suited to social media streams:  
* Short messages (microtexts): Twitter and most Facebook messages are very short (140 characters for tweets). Many semantic-based methods reviewed below supplement these with extra information and context coming from embedded URLs and hashtags. For instance, in the work of [5], the authors augment tweets by linking them to contemporaneous news articles, whereas in [6], online hashtag glossaries are exploited to augment tweets.