Sentiment by emoticons challenge

Give the probability of a positive sentiment for a short Polish text.

Git repo URL: git://gonito.net/sentiment-by-emoticons / Branch: master

(Browse at https://gonito.net/gitlist/sentiment-by-emoticons.git/master)

Leaderboard

# submitter when description test-A LogLoss ×
1 MF 2018-05-14 15:18 Vowpal Wabbit -b 27 --ngram 3 vowpal-wabbit logistic-regression 0.47412 12
2 [anonymised] 2018-05-13 20:33 ISI2018-024 - changed some parameters in vw model generation; changed flattening vowpal-wabbit logistic-regression 0.47525 11
3 [anonymised] 2018-05-13 21:31 vw 0.47659 18
4 Adam Kulczycki 2018-05-13 22:10 vowpal wabbit vowpal-wabbit logistic-regression 0.47690 9
5 MSz 2018-05-21 11:47 improved vowpa vowpal-wabbit logistic-regression 0.48053 11
6 Michał Szczepanowski 2018-05-13 20:41 vowpal-wabbit proba 1 vowpal-wabbit logistic-regression 0.48636 9
7 Przemysław Stawujak 2018-05-13 22:53 vowpal wabbit - 2 vowpal-wabbit logistic-regression 0.49103 13
8 Michał 2018-05-13 17:25 Volkswagen vowpal-wabbit logistic-regression 0.49399 6
9 [anonymised] 2018-05-29 09:26 task fix vowpal-wabbit logistic-regression 0.54511 5
10 p/tlen 2018-07-14 17:48 multiple passes=5 seed=3 0.54537 6
11 patrycja 2018-05-27 17:04 vowpal wabbit stemmed model 2 vowpal-wabbit logistic-regression stemming 0.54669 7
12 Weronika 2018-06-18 09:06 LSTM v4 all/500/515880(1)/256, 1 epoch neural-network 0.54739 40
13 Klaudia Wereniewicz 2018-05-27 21:48 fasttext predict-prob fasttext 0.55637 16
14 Micik2 2018-06-05 23:21 Zadanie029 wersja poprawiona vowpal-wabbit logistic-regression 0.57464 10
15 [anonymised] 2018-05-20 22:52 implent logistic regression with some ready-made solution (but not Vowpal Wabbit) ready-made logistic-regression 0.57783 27
16 [anonymised] 2019-01-27 14:36 dampie5 solution v4.2 0.58272 14
17 [anonymised] 2019-01-27 07:37 dodanie zaktualizowanego pliku python 0.58559 11
18 [anonymised] 2019-01-27 07:50 s402267 - sentiment-by-emoticons 0.58839 1
19 [anonymised] 2019-01-27 09:31 Init 0.58839 1
20 [anonymised] 2018-04-21 21:36 corrected 0.58845 5
21 [anonymised] 2018-04-20 15:06 naive-bayes partially-trained 0.58881 2
22 Karol Mazurek 2019-01-27 08:38 Brylantowa 4 0.59052 5
23 Ironus 2018-05-14 15:14 Logistic regresion with sklearn ready-made logistic-regression 0.59692 16
24 [anonymised] 2019-01-26 20:14 proba2 testA zadanie2 0.60223 10
25 [anonymised] 2019-01-26 20:50 Rozwiązanie zadania Sentiment by emoticons challenge 0.60473 1
26 [anonymised] 2019-02-21 17:22 Second solution 0.60789 2
27 [anonymised] 2019-01-27 11:13 Wersja milion 0.61030 4
28 [anonymised] 2019-01-27 09:42 dssad 0.61050 9
29 [anonymised] 2019-01-26 17:40 kd solution 0.61486 1
30 [anonymised] 2019-01-26 17:24 mk 0.61500 1
31 [anonymised] 2019-01-27 08:56 my second challenge solution 0.61509 1
32 [anonymised] 2019-01-26 20:13 My brilliant solution 0.61551 15
33 [anonymised] 2019-01-26 17:54 my solution 0.61579 2
34 Grzegorz Bąk 2019-01-24 12:32 rubinowa 8 0.62092 5
35 [anonymised] 2018-04-23 08:25 Saving model to file naive-bayes multinomial python self-made 0.62494 28
36 [anonymised] 2018-05-29 10:47 LogLoss and Accuracy charts naive-bayes analysis 0.62914 30
37 Patryk 2018-04-23 17:20 Fix lowerCase naive-bayes multinomial self-made java 0.63241 3
38 Joanna Janaszek 2018-06-12 14:34 zad40 kenlm lm 0.66313 1
39 Magda 2018-06-14 09:38 Poprawione zadanie 40 kenlm lm 0.66621 2
40 kan 2018-06-14 10:05 KenLM edited kenlm lm 0.67716 2
41 [anonymised] 2018-05-07 15:57 test included stupid better-than-no-model-baseline 0.69165 3
42 [anonymised] 2019-01-26 19:27 solution2 0.78228 4
43 Bogusz 2018-06-07 20:26 ready logistic regression Infinity 12
44 Marcin Gluza 2019-01-24 21:14 test2 Infinity 7

Graphs by parameters

passes

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seed

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