Twitter Sentiment Analysis

Guess the sentiment for texts in English.

# submitter when description dev-0 Accuracy dev-0 Likelihood test-A Accuracy test-A Likelihood
3 Kamig 2019-02-15 13:41 ulmfit-rem 0.85179 0.69895 0.84825 0.68958
1 Kamig 2019-02-15 12:33 ulmfit-textbugger (adversarials created on 30% of trainset) 0.86206 0.72654 0.86008 0.72071
2 Kamig 2019-02-15 12:10 ulmfit 0.85379 0.71831 0.85108 0.71186
18 [anonymised] 2019-01-27 22:20 Multinomial NB TfidfVectorizer without english stopwords 0.77330 0.60146 0.77242 0.60333
13 [anonymised] 2019-01-27 22:17 Bernoulli NB TfidfVectorizer without english stopwords 0.78649 0.60804 0.78729 0.60838
5 [anonymised] 2019-01-27 22:12 Logistic Regression TfidfVectorizer without english stopwords 0.80691 0.64968 0.80662 0.64890
17 [anonymised] 2019-01-27 22:05 Bernoulli NB TfidfVectorizer with removed numbers 0.76992 0.60318 0.77167 0.60449
11 [anonymised] 2019-01-27 22:01 Logistic Regression TfidfVectorizer with removed numbers 0.78276 0.62819 0.78154 0.62815
22 [anonymised] 2019-01-27 21:35 Multinomial NB TfidfVectorizer without removed punctation 0.76319 0.59878 0.76512 0.59979
9 [anonymised] 2019-01-27 21:34 Logistic Regression TfidfVectorizer without removed punctation 0.78507 0.63037 0.78462 0.62967
21 [anonymised] 2019-01-27 21:31 Bernoulli NB TfidfVectorizer without removed punctation 0.76938 0.60134 0.77129 0.60057
14 [anonymised] 2019-01-27 21:24 Bernoulli NB TfidfVectorizer(min_df=5, max_df=0.8) 0.76996 0.60453 0.77242 0.60594
12 [anonymised] 2019-01-27 21:22 Logistic Regression TfidfVectorizer(min_df=5, max_df=0.8) 0.78000 0.62732 0.77983 0.62726
20 [anonymised] 2019-01-27 21:21 Multinomial NB TfidfVectorizer(min_df=5, max_df=0.8) 0.76269 0.59937 0.76350 0.60112
23 [anonymised] 2019-01-27 21:15 Multinomial NB TfidfVectorizer 0.76088 0.59746 0.76208 0.59892
10 [anonymised] 2019-01-27 21:13 Logistic Regression TfidfVectorizer 0.78253 0.62812 0.78150 0.62845
19 [anonymised] 2019-01-27 21:11 Bernoulli NB TfidfVectorizer 0.77046 0.60293 0.77204 0.60332
28 [anonymised] 2019-01-27 21:08 linear_SVG TfidfVectorizer 0.77611 0.00000 0.77300 0.00000
6 [anonymised] 2019-01-08 01:38 LogisticRegression TfidfVectorizer without @-mentions 0.80295 0.64782 0.80388 0.64628
7 [anonymised] 2019-01-08 01:30 LogisticRegression TfidfVectorizer without @-mentions, URLs 0.80284 0.64713 0.80308 0.64548
8 [anonymised] 2019-01-08 01:25 LogisticRegression TfidfVectorizer without HTML tags, @-mentions, hash-tags, URLs, ... N/A N/A 0.80229 0.64400
4 [anonymised] 2019-01-07 23:58 LogisticRegression TfidfVectorizer 0.80665 0.65007 0.80796 0.64925
16 [anonymised] 2019-01-07 23:45 BernoulliNB TfidfVectorizer 0.78515 0.60497 0.78612 0.60469
27 [anonymised] 2019-01-07 23:38 NN: 10 Relu, 1 sigmoid - countvec 0.50108 0.49999 0.49750 0.49999
26 [anonymised] 2019-01-07 23:31 NN: 10 Relu, 1 sigmoid - tfidf 0.50104 0.50000 0.49825 0.50000
15 [anonymised] 2019-01-07 18:27 BernoulliNB 0.78515 0.60497 0.78612 0.60469
24 [anonymised] 2019-01-07 17:49 MultinomialNB 0.77815 0.58650 0.77825 0.58509
25 p/tlen 2018-12-18 06:57 Null model stupid null-model 0.49912 0.50000 0.50267 0.50000