Twitter Sentiment Analysis

Guess the sentiment for texts in English. [ver. 1.0.0]

# submitter when ver. description dev-0 Accuracy dev-0 Likelihood test-A Accuracy test-A Likelihood
207 [anonymized] 2020-06-21 16:20 1.0.0 roberta pretrained roberta N/A N/A 0.84529 0.00000
122 [anonymized] 2020-06-12 21:29 1.0.0 lstm solution lstm 0.76861 0.57364 0.76362 0.57162
90 [anonymized] 2020-06-01 06:46 1.0.0 v7 0.76619 0.60840 0.76688 0.60570
88 [anonymized] 2020-06-01 06:20 1.0.0 v6 0.77580 0.61465 0.77542 0.61176
89 [anonymized] 2020-05-31 19:48 1.0.0 v5.1 0.76857 0.60781 0.76850 0.60644
86 [anonymized] 2020-05-31 19:23 1.0.0 v5.1 ready-made svm 0.76857 0.61655 0.76850 0.61405
91 [anonymized] 2020-05-31 18:56 1.0.0 v4 0.75292 0.60348 0.75342 0.60224
99 [anonymized] 2020-05-31 18:51 1.0.0 v3 0.74631 0.59607 0.73962 0.59200
98 [anonymized] 2020-05-31 13:55 1.0.0 v2 0.74631 0.59607 0.73962 0.59200
206 [anonymized] 2020-05-31 13:38 1.0.0 v1 0.74619 0.00000 0.73925 0.00000
92 [anonymized] 2020-05-29 10:46 1.0.0 ISI-89, xgboost, ready-made, sklearn, dockerfile ready-made xgboost 0.76027 0.59905 0.75775 0.59717
148 [anonymized] 2020-05-25 19:37 1.0.0 IRSTLM v22 irstlm 0.60160 0.50471 0.59567 0.50327
157 [anonymized] 2020-05-25 13:58 1.0.0 IRSTLM v.3 0.42363 0.42954 0.42521 0.43012
1 kubapok 2020-05-20 08:31 1.0.0 roberta large finetunned 1 epoch 0.89902 0.78035 0.89950 0.77317
123 [anonymized] 2020-05-18 15:31 1.0.0 isrtlm first try irstlm 0.75704 0.56976 0.75750 0.56999
145 [anonymized] 2020-05-11 16:04 1.0.0 kenlm#15 kenlm 0.68893 0.00000 0.65150 0.52102
155 [anonymized] 2020-05-11 15:43 1.0.0 kenlm#14 kenlm 0.68893 0.00000 0.68862 0.44928
205 [anonymized] 2020-05-11 15:36 1.0.0 kenlm#13 kenlm 0.68893 0.00000 0.68867 0.00000
154 [anonymized] 2020-05-11 15:35 1.0.0 kenlm#12 kenlm 0.68893 0.00000 0.68862 0.44928
204 [anonymized] 2020-05-11 15:33 1.0.0 kenlm#11 kenlm 0.68893 0.00000 0.31138 0.00000
156 [anonymized] 2020-05-11 14:32 1.0.0 kenlm#10 kenlm 0.68893 0.00000 0.68862 0.44928
153 [anonymized] 2020-05-11 14:22 1.0.0 kenlm#9 kenlm 0.68893 0.00000 0.68862 0.44928
166 [anonymized] 2020-05-11 14:18 1.0.0 kenlm#8 kenlm 0.68893 0.00000 0.50267 0.31890
164 [anonymized] 2020-05-11 14:14 1.0.0 kenlm#7 kenlm 0.68893 0.00000 0.65150 0.36464
169 [anonymized] 2020-05-11 14:02 1.0.0 kenlm#6 kenlm 0.68893 0.00000 0.34850 0.23125
163 [anonymized] 2020-05-11 13:57 1.0.0 kenlm#5 kenlm 0.68893 0.00000 0.65150 0.36464
203 [anonymized] 2020-05-11 13:49 1.0.0 kenlm#4 kenlm 0.68893 0.00000 0.68862 0.00000
152 [anonymized] 2020-05-11 13:46 1.0.0 kenlm#3 kenlm 0.68893 0.00000 0.68862 0.44928
112 [anonymized] 2020-05-11 13:45 1.0.0 KenLM v1 kenlm 0.77992 0.58892 0.77804 0.58771
175 [anonymized] 2020-05-11 13:23 1.0.0 kenlm#2 kenlm 0.50088 0.00000 0.49733 0.00000
202 [anonymized] 2020-05-11 12:55 1.0.0 kenlm#1 kenlm 0.68893 0.00000 0.68862 0.00000
2 kubapok 2020-05-09 08:04 1.0.0 roberta base fairseq 2 epochs (can be finetuned more) 0.88894 0.76745 0.88800 0.76202
111 [anonymized] 2020-05-06 14:03 1.0.0 ISI-048 probabilities with kenlm kenlm 0.77992 0.58964 0.77812 0.58818
127 [anonymized] 2019-09-10 15:46 1.0.0 Wynik po 5000 słów w słowniku ze stopwords, Tokenizer, simple NN 0.71135 0.56499 0.70512 0.56303
126 [anonymized] 2019-09-10 15:25 1.0.0 Wynik po 15000 słowach w słowniku ze stopwords, Tokenizer, simple NN 0.71254 0.56556 0.71029 0.56369
125 [anonymized] 2019-09-10 15:23 1.0.0 Wynik po 5000 słowach w słowniku ze stopwords, Tokenizer, simple NN 0.71254 0.56556 0.70917 0.56396
143 [anonymized] 2019-09-09 12:38 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), NN’ 0.77219 0.52477 0.77362 0.52996
161 [anonymized] 2019-09-08 07:41 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Nearest_Neighbors3 0.66071 0.36590 0.66221 0.37560
174 [anonymized] 2019-09-08 06:08 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), KMeans 0.45647 0.00192 0.45192 0.00182
172 [anonymized] 2019-09-08 06:07 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Decision_Tree 0.72950 0.04761 0.73088 0.04868
171 [anonymized] 2019-09-08 06:07 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Linear_SVC 0.82041 0.12649 0.82212 0.12901
150 [anonymized] 2019-09-08 06:06 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Bernoulli_NB 0.78673 0.47508 0.78692 0.47318
62 [anonymized] 2019-09-08 06:06 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Multinomial_NB 0.79703 0.64342 0.79938 0.64442
158 [anonymized] 2019-09-06 13:53 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Nearest_Neighbors7 0.62556 0.40794 0.62754 0.40286
165 [anonymized] 2019-09-06 13:52 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Nearest_Neighbors5 0.63267 0.31527 0.63433 0.32120
160 [anonymized] 2019-09-06 13:52 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Nearest_Neighbors3 0.65901 0.37016 0.66104 0.38064
168 [anonymized] 2019-09-06 13:52 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Nearest_Neighbors2 0.61348 0.26318 0.61479 0.27322
173 [anonymized] 2019-09-06 12:44 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), KMeans 0.45658 0.00192 0.45242 0.00183
170 [anonymized] 2019-09-06 12:33 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Linear_SVC 0.82018 0.12615 0.82350 0.13107
146 [anonymized] 2019-09-06 11:58 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Decision_Tree 0.56653 0.51267 0.56800 0.51311
151 [anonymized] 2019-09-06 11:55 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Bernoulli_NB 0.78761 0.47338 0.78712 0.47164
60 [anonymized] 2019-09-06 11:55 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), Multinomial_NB 0.79795 0.64428 0.80058 0.64544
6 [anonymized] 2019-08-23 12:07 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82653 0.67385 0.82467 0.67126
7 [anonymized] 2019-08-23 12:07 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82668 0.67356 0.82408 0.67078
8 [anonymized] 2019-08-23 12:07 1.0.0 Wynik po 80000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82564 0.67325 0.82392 0.67029
14 [anonymized] 2019-08-23 12:06 1.0.0 Wynik po 70000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82472 0.67288 0.82150 0.66893
16 [anonymized] 2019-08-23 12:06 1.0.0 Wynik po 60000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82433 0.67221 0.82100 0.66788
18 [anonymized] 2019-08-23 12:06 1.0.0 Wynik po 50000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82345 0.67113 0.81975 0.66657
21 [anonymized] 2019-08-23 12:06 1.0.0 Wynik po 40000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.82164 0.66959 0.81883 0.66497
23 [anonymized] 2019-08-23 12:05 1.0.0 Wynik po 30000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.81907 0.66753 0.81746 0.66259
26 [anonymized] 2019-08-23 12:05 1.0.0 Wynik po 20000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.81603 0.66331 0.81425 0.65904
46 [anonymized] 2019-08-23 12:05 1.0.0 Wynik po 10000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression 0.80868 0.65426 0.80492 0.64995
31 [anonymized] 2019-08-23 12:02 1.0.0 Wynik po 100000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81784 0.65336 0.82150 0.65278
30 [anonymized] 2019-08-23 12:02 1.0.0 Wynik po 90000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81791 0.65430 0.82096 0.65289
29 [anonymized] 2019-08-23 12:02 1.0.0 Wynik po 80000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81772 0.65503 0.82175 0.65362
33 [anonymized] 2019-08-23 12:01 1.0.0 Wynik po 70000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81864 0.65660 0.81929 0.65260
32 [anonymized] 2019-08-23 12:01 1.0.0 Wynik po 60000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81853 0.65721 0.81917 0.65265
34 [anonymized] 2019-08-23 12:01 1.0.0 Wynik po 50000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81841 0.65701 0.81904 0.65219
39 [anonymized] 2019-08-23 12:01 1.0.0 Wynik po 40000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81807 0.65665 0.81800 0.65155
44 [anonymized] 2019-08-23 12:00 1.0.0 Wynik po 30000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81753 0.65531 0.81596 0.65016
49 [anonymized] 2019-08-23 12:00 1.0.0 Wynik po 20000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.81314 0.65233 0.81271 0.64708
64 [anonymized] 2019-08-23 12:00 1.0.0 Wynik po 10000 cech ze stopwords, CountVectorizer, ngram=(1,3), LogisticRegression 0.80653 0.64412 0.80446 0.63912
9 [anonymized] 2019-08-23 10:25 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82522 0.67286 0.82412 0.66997
12 [anonymized] 2019-08-23 10:24 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82499 0.67244 0.82350 0.66957
13 [anonymized] 2019-08-23 10:19 1.0.0 Wynik po 80000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82418 0.67209 0.82317 0.66943
15 [anonymized] 2019-08-23 10:19 1.0.0 Wynik po 70000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82276 0.67164 0.82229 0.66839
17 [anonymized] 2019-08-23 10:19 1.0.0 Wynik po 60000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82141 0.67120 0.82183 0.66770
19 [anonymized] 2019-08-23 10:18 1.0.0 Wynik po 50000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.82072 0.67050 0.82096 0.66609
22 [anonymized] 2019-08-23 10:18 1.0.0 Wynik po 40000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.81934 0.66944 0.81871 0.66455
25 [anonymized] 2019-08-23 10:18 1.0.0 Wynik po 30000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.81872 0.66751 0.81712 0.66244
27 [anonymized] 2019-08-23 10:18 1.0.0 Wynik po 20000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.81561 0.66416 0.81412 0.65880
43 [anonymized] 2019-08-23 10:17 1.0.0 Wynik po 10000 cech ze stopwords, TfidfVectorizer, ngram=(1,2), LogisticRegression 0.80707 0.65459 0.80617 0.65044
40 [anonymized] 2019-08-23 10:08 1.0.0 Wynik po 100000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81930 0.65335 0.82071 0.65133
41 [anonymized] 2019-08-23 10:08 1.0.0 Wynik po 90000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81895 0.65358 0.81967 0.65096
35 [anonymized] 2019-08-23 10:08 1.0.0 Wynik po 80000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81922 0.65405 0.82108 0.65209
37 [anonymized] 2019-08-23 10:07 1.0.0 Wynik po 70000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81818 0.65456 0.81879 0.65161
36 [anonymized] 2019-08-23 10:07 1.0.0 Wynik po 60000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81876 0.65509 0.81912 0.65198
42 [anonymized] 2019-08-23 10:07 1.0.0 Wynik po 50000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81891 0.65583 0.81683 0.65083
45 [anonymized] 2019-08-23 10:07 1.0.0 Wynik po 40000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81703 0.65559 0.81646 0.65009
47 [anonymized] 2019-08-23 10:07 1.0.0 Wynik po 30000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81491 0.65442 0.81571 0.64877
56 [anonymized] 2019-08-23 10:06 1.0.0 Wynik po 20000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.81426 0.65183 0.81238 0.64613
65 [anonymized] 2019-08-23 10:06 1.0.0 Wynik po 10000 cech ze stopwords, CountVectorizer, ngram=(1,2), LogisticRegression 0.80557 0.64349 0.80600 0.63895
87 [anonymized] 2019-08-23 10:00 1.0.0 Wynik po 10000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77388 0.61620 0.77229 0.61332
81 [anonymized] 2019-08-23 09:55 1.0.0 Wynik po 100000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77765 0.61761 0.77654 0.61654
82 [anonymized] 2019-08-23 09:55 1.0.0 Wynik po 90000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77780 0.61757 0.77633 0.61653
80 [anonymized] 2019-08-23 09:54 1.0.0 Wynik po 80000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77761 0.61759 0.77633 0.61655
79 [anonymized] 2019-08-23 09:54 1.0.0 Wynik po 70000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77711 0.61757 0.77638 0.61657
77 [anonymized] 2019-08-23 09:54 1.0.0 Wynik po 60000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77734 0.61770 0.77658 0.61662
78 [anonymized] 2019-08-23 09:54 1.0.0 Wynik po 50000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77723 0.61759 0.77692 0.61657
83 [anonymized] 2019-08-23 09:53 1.0.0 Wynik po 40000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77734 0.61750 0.77642 0.61645
84 [anonymized] 2019-08-23 09:53 1.0.0 Wynik po 30000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77727 0.61785 0.77696 0.61632
85 [anonymized] 2019-08-23 09:53 1.0.0 Wynik po 20000 cech BEZ stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.77607 0.61731 0.77700 0.61592
54 [anonymized] 2019-08-23 09:33 1.0.0 Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80257 0.64730 0.80279 0.64629
51 [anonymized] 2019-08-23 09:33 1.0.0 Wynik po 90000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80238 0.64731 0.80279 0.64637
52 [anonymized] 2019-08-23 09:33 1.0.0 Wynik po 80000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80257 0.64738 0.80279 0.64635
53 [anonymized] 2019-08-23 09:32 1.0.0 Wynik po 70000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80288 0.64736 0.80296 0.64633
55 [anonymized] 2019-08-23 09:32 1.0.0 Wynik po 60000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80272 0.64734 0.80333 0.64621
57 [anonymized] 2019-08-23 09:32 1.0.0 Wynik po 50000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80280 0.64726 0.80300 0.64598
58 [anonymized] 2019-08-23 09:32 1.0.0 Wynik po 40000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80242 0.64726 0.80271 0.64588
59 [anonymized] 2019-08-23 09:31 1.0.0 Wynik po 30000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80118 0.64711 0.80279 0.64565
61 [anonymized] 2019-08-23 09:31 1.0.0 Wynik po 20000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.80076 0.64635 0.80167 0.64512
63 [anonymized] 2019-08-23 09:30 1.0.0 Wynik po 10000 cech ze stopwords, TfidfVectorizer, ngram=(1,1), LogisticRegression 0.79945 0.64469 0.79738 0.64133
70 [anonymized] 2019-08-23 09:16 1.0.0 Wynik po 100000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80180 0.63425 0.80092 0.63221
68 [anonymized] 2019-08-23 09:15 1.0.0 Wynik po 90000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80130 0.63418 0.80092 0.63229
69 [anonymized] 2019-08-23 09:15 1.0.0 Wynik po 80000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80111 0.63422 0.80079 0.63227
66 [anonymized] 2019-08-23 09:15 1.0.0 Wynik po 70000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80103 0.63420 0.80100 0.63237
67 [anonymized] 2019-08-23 09:15 1.0.0 Wynik po 60000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80149 0.63430 0.80083 0.63234
71 [anonymized] 2019-08-23 09:14 1.0.0 Wynik po 50000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80142 0.63415 0.80025 0.63218
73 [anonymized] 2019-08-23 09:14 1.0.0 Wynik po 40000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80095 0.63418 0.80017 0.63200
74 [anonymized] 2019-08-23 09:13 1.0.0 Wynik po 30000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80080 0.63455 0.80029 0.63199
72 [anonymized] 2019-08-23 09:13 1.0.0 Wynik po 20000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.80095 0.63421 0.80021 0.63206
75 [anonymized] 2019-08-23 09:10 1.0.0 Wynik po 10000 cech ze stopwords, CountVectorizer, ngram=(1,1), LogisticRegression 0.79961 0.63330 0.79612 0.62940
119 [anonymized] 2019-06-17 13:04 1.0.0 selfmade_bayes1.py naive-bayes self-made 0.76707 0.58113 0.76867 0.58223
104 [anonymized] 2019-06-17 11:46 1.0.0 readymade_naive-bayes.py naive-bayes ready-made 0.78373 0.59324 0.78125 0.59135
201 [anonymized] 2019-06-16 09:15 1.0.0 dk tw readymade naive-bayes self-made 1.00000 1.00000 0.50100 0.00000
200 [anonymized] 2019-06-16 09:05 1.0.0 Achievement07 naive-bayes ready-made N/A N/A 0.78267 0.00000
199 [anonymized] 2019-06-16 07:44 1.0.0 twitter ready made naive-bayes ready-made 0.76930 0.00000 0.76962 0.00000
198 [anonymized] 2019-06-15 23:46 1.0.0 Twitter Bayes naive-bayes ready-made N/A N/A 0.78371 0.00000
197 [anonymized] 2019-06-15 14:13 1.0.0 ready naiwny faktyczny naive-bayes ready-made 0.78361 0.00000 0.78129 0.00000
96 [anonymized] 2019-06-11 16:03 1.0.0 Fasttext v5 fasttext 0.75838 0.59275 0.75483 0.59278
196 [anonymized] 2019-06-11 15:41 1.0.0 Fasttext 0.75811 0.00000 0.75679 0.00000
11 [anonymized] 2019-06-11 15:26 1.0.0 vw script added vowpal-wabbit 0.82030 0.66694 0.81846 0.66975
195 [anonymized] 2019-06-11 15:20 1.0.0 word2vec logistic-regression word2vec 0.60760 0.00000 0.60704 0.00000
10 [anonymized] 2019-06-09 12:07 1.0.0 VW v2. (nn, 2gram, 2skips, 20 passes, log loss function, qe) vowpal-wabbit 0.82030 0.66694 0.81846 0.66975
116 [anonymized] 2019-06-09 11:22 1.0.0 Zajecia Bayes final4 naive-bayes multinomial self-made 0.78588 0.58386 0.78442 0.58313
115 [anonymized] 2019-06-09 11:03 1.0.0 Zajecia Bayes final3 0.78588 0.58386 0.78442 0.58313
28 [anonymized] 2019-06-09 10:36 1.0.0 vw (10 layers nn,4gram,logistic loss funtion, 10 passes) 0.81168 0.65556 0.80938 0.65462
213 [anonymized] 2019-06-09 10:23 1.0.0 Merge branch 'master' of ssh://gonito.net/s452111/twitter-sentiment-analysis 0.81168 0.65556 N/A N/A
212 [anonymized] 2019-06-09 09:50 1.0.0 vw with 10 layers nn, -b 27, passes 10, quadratic, logistic loss function, 4-gram 0.81168 0.65556 N/A N/A
20 [anonymized] 2019-06-08 15:10 1.0.0 Vowpal Wabbit logistic + ngram vowpal-wabbit 0.81853 0.66535 0.81529 0.66585
38 [anonymized] 2019-06-08 14:01 1.0.0 Vowpal Wabbit logistic + ngram vowpal-wabbit N/A N/A 0.80375 0.65156
107 [anonymized] 2019-06-04 22:16 1.0.0 solution 2e naive-bayes multinomial self-made 0.78276 0.59076 0.78242 0.59054
95 [anonymized] 2019-06-04 11:04 1.0.0 naive bayes - fine-tuned naive-bayes multinomial self-made probabilities 0.78569 0.59475 0.78404 0.59348
159 [anonymized] 2019-06-02 10:46 1.0.0 naiwny naive-bayes self-made 0.50088 0.38470 0.49733 0.38264
194 [anonymized] 2019-06-02 08:33 1.0.0 Tweeter - self-made z zajęć 0.21735 0.00000 0.21725 0.00000
193 [anonymized] 2019-06-01 20:46 1.0.0 zadanie z twitterem naive-bayes self-made 0.21735 0.00000 0.21725 0.00000
192 [anonymized] 2019-06-01 20:26 1.0.0 naive bayes ready made naive-bayes ready-made 0.78353 0.00000 0.78146 0.00000
191 [anonymized] 2019-06-01 17:34 1.0.0 my solution naive-bayes self-made N/A N/A 0.49742 0.00000
190 [anonymized] 2019-06-01 16:51 1.0.0 bayes gotowy naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
211 [anonymized] 2019-06-01 16:29 1.0.0 rozwiazanie bayesa naive-bayes ready-made N/A N/A N/A N/A
189 [anonymized] 2019-06-01 14:35 1.0.0 naive bayes naive-bayes self-made 0.21735 0.00000 0.21725 0.00000
105 [anonymized] 2019-06-01 13:47 1.0.0 Solution naive-bayes self-made N/A N/A 0.78317 0.59102
109 [anonymized] 2019-06-01 13:06 1.0.0 Solution naive-bayes ready-made 0.78357 0.59313 0.77958 0.59009
93 [anonymized] 2019-06-01 08:03 1.0.0 self-made_naive-bayes naive-bayes self-made 0.78653 0.59538 0.78696 0.59572
188 [anonymized] 2019-05-31 18:30 1.0.0 twutter naive-bayes ready-made 0.77592 0.00000 0.77479 0.00000
142 [anonymized] 2019-05-30 14:17 1.0.0 fourth commit, naive bayes, ready-made naive-bayes ready-made 0.78373 0.53658 0.78125 0.53401
131 [anonymized] 2019-05-30 13:36 1.0.0 third commit, naive bayes, self-made 0.78188 0.55732 0.78154 0.55690
129 [anonymized] 2019-05-30 12:45 1.0.0 second commit, naive bayes, self-made naive-bayes self-made 0.78234 0.55788 0.78250 0.55808
187 [anonymized] 2019-05-30 12:41 1.0.0 first commit, naive bayes, self-made 0.78234 0.00000 0.78250 0.00000
186 [anonymized] 2019-05-29 16:48 1.0.0 bayes ready made naive-bayes ready-made 0.52611 0.00000 0.51829 0.00000
113 [anonymized] 2019-05-28 19:50 1.0.0 naive-one naive-bayes self-made 0.77292 0.58441 0.77238 0.58406
117 [anonymized] 2019-05-28 19:48 1.0.0 naive-one naive-bayes self-made 0.77023 0.58268 0.77062 0.58294
102 [anonymized] 2019-05-28 19:35 1.0.0 naive-one naive-bayes self-made 0.78569 0.59266 0.78404 0.59159
120 [anonymized] 2019-05-28 19:29 1.0.0 naive-one naive-bayes self-made 0.77196 0.57701 0.77312 0.57758
167 [anonymized] 2019-05-28 19:22 1.0.0 naive-one naive-bayes self-made 0.73835 0.29749 0.73950 0.29907
185 [anonymized] 2019-05-28 18:52 1.0.0 bayes self made naive-bayes self-made 0.78519 0.00000 0.78371 0.00000
210 [anonymized] 2019-05-28 18:29 1.0.0 bayes self 0.00000 N/A 0.00000 N/A
24 [anonymized] 2019-05-28 17:59 1.0.0 vw with -nn --ngram and linear regression vowpal-wabbit 0.81676 0.66360 0.81492 0.66244
50 [anonymized] 2019-05-28 17:52 1.0.0 vm with -nn and linear regression 0.80130 0.65116 0.79838 0.64671
48 [anonymized] 2019-05-28 17:48 1.0.0 vm with -nn and linear regression 0.80380 0.65082 0.80046 0.64857
184 [anonymized] 2019-05-27 22:36 1.0.0 naive-one naive-bayes self-made 0.78223 0.00000 0.78179 0.00000
108 [anonymized] 2019-05-27 17:25 1.0.0 Naive bayes - selfmade2 naive-bayes self-made 0.78238 0.59067 0.78150 0.59042
183 [anonymized] 2019-05-26 15:42 1.0.0 Naive Bayes - ready made naive-bayes ready-made 0.77592 0.00000 0.77479 0.00000
121 [anonymized] 2019-05-26 13:40 1.0.0 naive bayes with bpe naive-bayes bpe 0.76692 0.57771 0.76496 0.57607
182 [anonymized] 2019-05-24 09:09 1.0.0 NB sig 3 naive-bayes self-made 0.76573 0.00000 0.76638 0.00000
118 [anonymized] 2019-05-21 11:27 1.0.0 naive-bayes-selfmade naive-bayes self-made 0.76707 0.58113 0.76867 0.58223
181 [anonymized] 2019-05-19 12:49 1.0.0 Bernoulii Bayes naive-bayes ready-made 0.78230 0.00000 0.78512 0.00000
180 [anonymized] 2019-05-19 12:11 1.0.0 tweet naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
103 [anonymized] 2019-05-19 11:35 1.0.0 naive-bayes-readymade naive-bayes ready-made 0.78373 0.59324 0.78125 0.59135
179 [anonymized] 2019-05-19 11:31 1.0.0 tweety naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
147 [anonymized] 2019-05-18 02:42 1.0.0 naive-bayes multino naive-bayes multinomial self-made 0.78561 0.50533 0.78675 0.50703
137 [anonymized] 2019-05-17 19:03 1.0.0 Most positives and most negatives words naive-bayes multinomial 0.76819 0.54617 0.78275 0.54941
144 [anonymized] 2019-05-13 15:20 1.0.0 test-A naive-bayes multinomial self-made 0.76115 0.53251 0.75754 0.52830
114 [anonymized] 2019-05-08 12:06 1.0.0 naive bayes naive-bayes multinomial self-made 0.78588 0.58386 0.78442 0.58313
94 [anonymized] 2019-05-08 10:43 1.0.0 hackaton naive-bayes multinomial self-made 0.78265 0.59347 0.78275 0.59402
106 [anonymized] 2019-05-08 10:28 1.0.0 hackaton 0.78265 0.59069 0.78275 0.59075
139 [anonymized] 2019-05-07 21:27 1.0.0 lower() naive-bayes multinomial self-made N/A N/A 0.76379 0.54520
140 [anonymized] 2019-05-07 19:18 1.0.0 solution naive-bayes multinomial self-made 0.76857 0.54626 0.76362 0.54516
135 [anonymized] 2019-05-07 16:54 1.0.0 simple NB naive-bayes multinomial self-made 0.78569 0.55006 0.78404 0.54970
141 [anonymized] 2019-05-07 16:51 1.0.0 naive bayes - lowercase naive-bayes multinomial self-made 0.76811 0.54616 0.76362 0.54516
136 [anonymized] 2019-05-07 16:40 1.0.0 Naive bayes naive-bayes multinomial self-made 0.78265 0.54939 0.78275 0.54941
138 [anonymized] 2019-05-07 16:38 1.0.0 classes naive-bayes multinomial self-made 0.78215 0.00000 0.78146 0.54912
124 [anonymized] 2019-05-07 12:37 1.0.0 Naive Bayes naive-bayes multinomial self-made 0.76796 0.56835 0.76388 0.56434
162 [anonymized] 2019-05-07 12:13 1.0.0 Bayes zajęcia naive-bayes multinomial self-made 0.78588 0.37011 0.78442 0.36763
178 [anonymized] 2019-05-07 12:02 1.0.0 solution naive-bayes multinomial self-made 0.78276 0.00000 0.78242 0.00000
97 [anonymized] 2019-05-06 19:50 1.0.0 implement multinomial Naive Bayes ... on labs naive-bayes multinomial self-made 0.78276 0.59250 0.78242 0.59224
101 [anonymized] 2019-05-06 19:43 1.0.0 implement multinomial Naive Bayes ... on labs naive-bayes multinomial self-made 0.78276 0.59197 0.78242 0.59169
100 [anonymized] 2019-05-06 18:41 1.0.0 bayes with probabilities naive-bayes multinomial self-made 0.78276 0.59197 0.78242 0.59169
209 [anonymized] 2019-05-06 17:03 1.0.0 poprawa Likelyhood naive-bayes multinomial self-made 0.76115 0.53251 N/A N/A
208 [anonymized] 2019-05-06 16:58 1.0.0 Zajecia 0.76115 0.00000 N/A N/A
177 [anonymized] 2019-05-06 16:55 1.0.0 bayes naive-bayes multinomial self-made 0.78276 0.00000 0.78242 0.00000
132 [anonymized] 2019-05-06 16:51 1.0.0 simple bayes with probs naive-bayes multinomial self-made 0.78353 0.55935 0.78154 0.55690
134 [anonymized] 2019-05-06 16:44 1.0.0 bayes first try naive-bayes multinomial self-made 0.78311 0.55883 0.77975 0.55472
128 Artur Nowakowski 2019-05-06 16:44 1.0.0 naive bayes naive-bayes multinomial self-made 0.78473 0.56081 0.78271 0.55833
133 [anonymized] 2019-05-06 16:42 1.0.0 bayes first try 0.78311 0.00000 0.77975 0.55472
130 Artur Nowakowski 2019-05-06 16:40 1.0.0 naive bayes 0.78357 0.55939 0.78217 0.55767
110 [anonymized] 2019-05-06 16:39 1.0.0 naive bayes ZAJECIA naive-bayes multinomial self-made 0.78569 0.58986 0.78404 0.58889
176 [anonymized] 2019-05-06 16:37 1.0.0 naive bayes ZAJECIA 0.78569 0.00000 0.78404 0.00000
76 [anonymized] 2019-03-21 12:23 1.0.0 tcn 0.79507 0.62366 0.79421 0.62513
5 [anonymized] 2019-02-15 13:41 1.0.0 ulmfit-rem 0.85179 0.69895 0.84825 0.68958
3 [anonymized] 2019-02-15 12:33 1.0.0 ulmfit-textbugger (adversarials created on 30% of trainset) 0.86206 0.72654 0.86008 0.72071
4 [anonymized] 2019-02-15 12:10 1.0.0 ulmfit 0.85379 0.71831 0.85108 0.71186
149 p/tlen 2018-12-18 06:57 1.0.0 Null model stupid null-model 0.49912 0.50000 0.50267 0.50000