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→Beliefs Update
*Digging a little deeper beyond this superficial exchange, we come to a point where we need to think qualitatively about tweets. What people care about reflects some material facts about their material selves. Tweets containing hashtags such as #kpceoworkshop, for instance, tells us which people are attending the event physically and which people are passing commentary on it. When a startup has an IPO/Acquisition, it will attract a tremendous volume of mentions. When the presidential candidates talk about their technology policies, the entrepreneurship twitterverse responds.
<blockquote>This is the next level of Twitter mining, often associated with Natural Language Processing techniques: '''Tweet Analytics'''. From tweets, we can learn about events that are unfolding in the entrepreneurship world, as well as new organizations that appear in the conversation. When measured over time, we can get a handle on the up-and-coming stars in the field, and emerging trends that are of note.</blockquote>
*On a even more physical level, tweets contain geo-information such as @user's home location and the tweet-from location. Through this, we stand to learn about the people's interests stratified by location. When combined with the former two forms of twitter mining, it can enhance what we know about physically-bound social dynamics and physically-bound shifts in interest and opinions.
<blockquote>'''Geo Visualization''' is the process of mapping tweets to a real map of the Earth. Applying '''tweet analytics''' and '''network visualization''' to it, we stand to have a better picture of the ongoings in terms of peoeple, organizations and events in particular places, for instance Palo Alto, CA or Austin, TX. When measured over time, we can observe the crests and troughs of activity in these places. This would be extremely promising especially for the '''HUBS''' research project.
==Twitter Mining==
