Twitter Sentiment Analysis
Guess the sentiment for texts in English. [ver. 1.0.0]
This is a long list of all submissions, if you want to see only the best, click leaderboard.
# | 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 |