As shown in Table9 (K is the number of predefined topics), PLSA is not quite effective mainly because traditional topic modeling approaches do not perform well on short texts from social media .

The proliferation of online social media significantly expedites this evolution, as new phrases triggered by social events may be disseminated rapidly in social media .

To automatically analyze such fast evolving language in social media , new computational models are demanded.

We believe that successful resolution of morphs is a crucial step for automated understanding of the fast evolving social media language, which is important for social media marketing (Bar-wise and Meehan, 2010).

To analyze social media behavior under active censorship, (Bamman et al., 2012) automatically discovered politically sensitive terms from Chinese tweets based on message deletion analysis.

Unfortunately the state-of-the-art techniques for these tasks still perform poorly on social media in terms of both accuracy and coverage of important information, these sophisticated semantic links all produced negative impact on the target ranking performance.

In contrast, users are less restricted in some other uncensored social media such as Twitter.

Because of such social correlation, close social neighbors in social media such as Twitter and Weibo may post similar information, or share similar opinion.

There are some chronic critics who always complain about the entity in social media .

In social media , most comments are informal, and, there are sarcastic and incomplete contexts.

As an alternative approach for social media , we can assume that users who share the same opinions will link to each other.

On a social media website, there may be millions of users and large numbers of comments.

The comments in social media are related to the real world in such fields as marketing and politics.

Analyzing comments in social media has been shown to be effective in predicting the behaviors of stock markets and of voters in elections (Bollen et al., 2011; Tumasjan et al., 2010; O’Connor et al., 2010).

We study subjective language in social media and create Twitter-specific lexicons via bootstrapping sentiment-bearing terms from multilingual Twitter streams.

Our experiments on English, Spanish and Russian show that the resulting lexicons are effective for sentiment classification for many under-explored languages in social media .

This is true for well-formed data, such as news and reviews, and it is particularly true for data from social media .

Communication in social media is informal, abbreviations and misspellings abound, and the person communicating is often trying to be funny, creative, and entertaining.

The dynamic nature of social media together with the extreme diversity of subjective language has implications for any system with the goal of analyzing sentiment in this domain.

However, the lexical resources that dictionary-based methods need, do not yet exist for the majority of languages in social media .

We describe a novel approach for automatically predicting the hidden demographic properties of social media users.

5While we used an “off the shelf” POS tagger in this work, we note that taggers optimized specifically for social media are now available and would likely have resulted in higher tagging accuracy (e. g. Owoputi et al.

There has been growing interest in characterizing social media users based on the content they generate; that is, automatically labeling users with demographic categories such as age and gender (Burger and Henderson, 2006; Schler et al., 2006; Rao et al., 2010; Mukherjee and Liu, 2010; Pennacchiotti and Popescu, 2011; Burger et al., 2011; Van Durme, 2012).

t0 Characterize Social Media Users

A leg is a relevant and correct part of both a male and a female (and many other living and inanimate objects), but it does not help us distinguish males from females in social media .

Many recent papers have analyzed the language of social media users, along dimensions such as ethnicity (Eisenstein et al., 2011; Rao et al., 2011; Pennacchiotti and Popescu, 2011; Fink et al., 2012) time zone (Kiciman, 2010), political orientation (Rao et al., 2010; Pennacchiotti and Popescu, 2011) and gender (Rao et al., 2010; Burger et al., 2011; Van Durme, 2012).

7Note that it is possible to achieve even higher performance on gender classification in social media if you have further information about a user, such as their full first and last name (Burger et al., 2011; Bergsma et al., 2013).

This is important because having thousands of gold standard annotations for every possible user characterization task, in every domain and social media platform, is not realistic.

Using a combination of content and username features “represents a use case common to many different social media sites, such as chat rooms and news article comment streams” (Burger et al., 2011).

We can therefore benchmark our approach against state-of-the-art supervised systems trained with plentiful gold-standard data, giving us an idea of how well our Bootstrapped system might compare to theoretically top-performing systems on other tasks, domains, and social media platforms where such gold-standard training data is not available.

Social Media contain a multitude of user opinions which can be used to predict real-world phenomena in many domains including politics, finance and health.

These techniques require very careful filtering of the input texts, as most Social Media posts are irrelevant to the task.

For the evaluation of the proposed methodologies we have created two data sets of Social Media content with different characteristics based in the UK and Austria respectively.

Data processing is performed using the TrendMiner architecture for Social Media analysis (Preotiuc-Pietro et al., 2012).

Web Social Media platforms have ushered a new era in human interaction and communication.

The textual content posted on Social Media platforms unarguably contains valuable information, but quite often it is hidden under vast amounts of unstructured user generated input.

The topic of political opinion mining from Social Media has been the focus of various recent research works.

Social media websites such as Twitter, Facebook, etc., have become ubiquitous platforms for social networking and content sharing.

Some recent researches suggest that news and social media such as blogs, micro-blogs, etc., can be analyzed to extract public sentiments to help predict the market (La-vrenko et al., 2000; Schumaker and Chen, 2009).

The topics mostly focus on hot keywords like: news, stocknews, earning, report, which stimulate active discussions on the social media platform.