The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity negativity) of mentions to political actors across sources, types of sources, and across time.
The majority of the work using social media data applied to political scenarios consist in ad hoc studies where researchers collect data from a given social network during a pre-defined period of time and produce their specific analysis or predictions. The availability of open source research platforms in this area is scarse. Usually researchers use specific APIs and software modules to produce their studies. One research platform is Trendminer @cite_12 , an open source platform for real time analysis of Twitter data. It is the most similar work to POPmine although it only focus on Twitter and does not include sentiment analysis module, while POPmine allows cross media data collection and analysis, including sentiment.
@cite_12
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The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold. Social media is changing existing information behavior by giving users access to real-time online information channels without the constraints of time and space. Social media, therefore, has created an enormous data analysis challenge for scientists trying to keep pace with developments in their field. Most previous studies have adopted broad-brush approaches that typically result in limited analysis possibilities. To address this problem, we applied text-mining techniques to Twitter data related to the 2012 Korean presidential election. We use three primary techniques: topic modeling to track changes in topical trends, mention-direction-based user network analysis, and term co-occurrence retrieval for further content analysis. Our study reveals that Twitter could be a useful way to detect and trace the advent of and changes in social issues, while analyzing mention-based user networks could show different aspects of user behaviors. The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content. With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.