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Rostlab / JS16_ProjectD_Group4

Joffrey Baratheon is one of the most loathed characters in TV history. As a matter of fact people were celebrating his TV death on Twitter. We are interested to learn more on how people feel about different characters by analyzing tweets mentioning GoT characters. In this project you will be analyzing Twitter feeds across a timeline, you will look for the name of GoT characters in that feed and try to identify whether the tweet is positive or negative. You can then generate a metric that evaluates what is the accumulated sentiment expressed on Twitter for that given character at a given point in time, and what is the trend (positive, negative). It will be interesting to intersect the sentiments for characters following the airing of a certain episode (you can easily get the airing date for an episode from the database constructed in Project A).
GNU General Public License v3.0
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Aggregator reloaded #91

Closed julienschmidt closed 8 years ago

julienschmidt commented 8 years ago

Fixes #89 Everything went better than expected

julienschmidt commented 8 years ago

For smaller datasets there is a clear overhead, but at least it works now :tada:

julienschmidt commented 8 years ago

Just discovered this option: http://mongoosejs.com/docs/api.html#query_Query-lean

https://github.com/Rostlab/JS16_ProjectD_Group4/pull/91/commits/4d956f4dc2773b180e31c8ce2955390668ffa53e noticeably reduces the overhead per tweet