This is an academic research project and for the most part I'm focusing on academic resources. But I'm working to understand the specific tools and methods for mining social media data in order to effectively intervene using communications campaigns. The annotation offered here adds to these concepts by introducing "community mining" and techniques for analyzing key players, roles, and strong subgroup connections of communication and influence within a larger social media network. These are key concepts for understanding how opinions within a network are formed, shared, and spread. Or as someone might have once said, it's about influencing a group by influencing the influencers.
I had no sense of how much research has been done on sentiment analysis, opinion mining, and machine learning. I suppose major progress had to wait until there were massive datasets of text content available to process. The explosion of data on social media has provided both the impetus and the fodder to develop increasingly sophisticated techniques that are beginning to actually work. In this annotation I present an overview of the techniques and tools of comparative opinion mining. It's great to know what people feel and think about one thing. It's probably twice as great to know what they think about two things. Like two different cars, guitars, or political candidates.
The phrase "sentiment analysis" is high on the list of search terms for anyone seeking to understand how to process social media data. It's a component of Natural Language Processing (NLP), where a machine extracts (somewhat) accurate meaning from human language and textual information. This seems really hard, unless I'm wrong, because the whole AI field and NLP seem to be moving forward fast once again. Here's an annotation of a journal article that provides a decent overview.