linear models

stroykova · Oct 27, 2018 · 22742ab · 22742ab
1 parent 20d6866
commit 22742ab
Show file tree

Hide file tree

Showing 15 changed files with 5,595 additions and 0 deletions.
diff --git a/2018-09/lecture_05_linear/images/dir-der.gif b/2018-09/lecture_05_linear/images/dir-der.gif
diff --git a/2018-09/lecture_05_linear/images/gridsearch.jpeg b/2018-09/lecture_05_linear/images/gridsearch.jpeg
diff --git a/2018-09/lecture_05_linear/images/header.png b/2018-09/lecture_05_linear/images/header.png
diff --git a/2018-09/lecture_05_linear/images/margin.png b/2018-09/lecture_05_linear/images/margin.png
diff --git a/2018-09/lecture_05_linear/images/opt1.gif b/2018-09/lecture_05_linear/images/opt1.gif
diff --git a/2018-09/lecture_05_linear/images/opt2.gif b/2018-09/lecture_05_linear/images/opt2.gif
diff --git a/2018-09/lecture_05_linear/images/overfitting.jpg b/2018-09/lecture_05_linear/images/overfitting.jpg
diff --git a/2018-09/lecture_05_linear/images/prob.png b/2018-09/lecture_05_linear/images/prob.png
diff --git a/2018-09/lecture_05_linear/images/regul.jpg b/2018-09/lecture_05_linear/images/regul.jpg
diff --git a/2018-09/lecture_05_linear/lecture-linear-models.ipynb b/2018-09/lecture_05_linear/lecture-linear-models.ipynb
diff --git a/2018-09/lecture_05_linear/lecture-linear-pract.ipynb b/2018-09/lecture_05_linear/lecture-linear-pract.ipynb
diff --git a/2018-09/lecture_05_linear/sentiment/amazon_cells_labelled.txt b/2018-09/lecture_05_linear/sentiment/amazon_cells_labelled.txt
diff --git a/2018-09/lecture_05_linear/sentiment/imdb_labelled.txt b/2018-09/lecture_05_linear/sentiment/imdb_labelled.txt
diff --git a/2018-09/lecture_05_linear/sentiment/readme.txt b/2018-09/lecture_05_linear/sentiment/readme.txt
@@ -0,0 +1,31 @@
+This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015
+Please cite the paper if you want to use it :)
+
+It contains sentences labelled with positive or negative sentiment, extracted from reviews of products, movies, and restaurants
+
+=======
+Format:
+=======
+sentence \t score \n
+
+
+=======
+Details:
+=======
+Score is either 1 (for positive) or 0 (for negative)	
+The sentences come from three different websites/fields:
+
+imdb.com
+amazon.com
+yelp.com
+
+For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. 
+We attempted to select sentences that have a clearly positive or negative connotaton, the goal was for no neutral sentences to be selected.
+
+
+
+For the full datasets look:
+
+imdb: Maas et. al., 2011 'Learning word vectors for sentiment analysis'
+amazon: McAuley et. al., 2013 'Hidden factors and hidden topics: Understanding rating dimensions with review text'
+yelp: Yelp dataset challenge http://www.yelp.com/dataset_challenge
diff --git a/2018-09/lecture_05_linear/sentiment/yelp_labelled.txt b/2018-09/lecture_05_linear/sentiment/yelp_labelled.txt