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
56 Adam Grochowski 2019-06-17 13:04 selfmade_bayes1.py naive-bayes self-made 0.76707 0.58113 0.76867 0.58223
42 Adam Grochowski 2019-06-17 11:46 readymade_naive-bayes.py naive-bayes ready-made 0.78373 0.59324 0.78125 0.59135
109 Dominik Kowal 2019-06-16 09:15 dk tw readymade naive-bayes self-made 1.00000 1.00000 0.50100 0.00000
108 JDM 2019-06-16 09:05 Achievement07 naive-bayes ready-made N/A N/A 0.78267 0.00000
107 Jagoda 2019-06-16 07:44 twitter ready made naive-bayes ready-made 0.76930 0.00000 0.76962 0.00000
106 Ewelina J 2019-06-15 23:46 Twitter Bayes naive-bayes ready-made N/A N/A 0.78371 0.00000
105 tomfur 2019-06-15 14:13 ready naiwny faktyczny naive-bayes ready-made 0.78361 0.00000 0.78129 0.00000
36 Damian Michalski 2019-06-11 16:03 Fasttext v5 fasttext 0.75838 0.59275 0.75483 0.59278
104 Damian Michalski 2019-06-11 15:41 Fasttext 0.75811 0.00000 0.75679 0.00000
5 pzimny 2019-06-11 15:26 vw script added vowpal-wabbit 0.82030 0.66694 0.81846 0.66975
103 Adrian Witczak 2019-06-11 15:20 word2vec logistic-regression word2vec 0.60760 0.00000 0.60704 0.00000
4 pzimny 2019-06-09 12:07 VW v2. (nn, 2gram, 2skips, 20 passes, log loss function, qe) vowpal-wabbit 0.82030 0.66694 0.81846 0.66975
53 [anonymised] 2019-06-09 11:22 Zajecia Bayes final4 naive-bayes multinomial self-made 0.78588 0.58386 0.78442 0.58313
52 [anonymised] 2019-06-09 11:03 Zajecia Bayes final3 0.78588 0.58386 0.78442 0.58313
8 pzimny 2019-06-09 10:36 vw (10 layers nn,4gram,logistic loss funtion, 10 passes) 0.81168 0.65556 0.80938 0.65462
115 pzimny 2019-06-09 10:23 Merge branch 'master' of ssh://gonito.net/s452111/twitter-sentiment-analysis 0.81168 0.65556 N/A N/A
114 pzimny 2019-06-09 09:50 vw with 10 layers nn, -b 27, passes 10, quadratic, logistic loss function, 4-gram 0.81168 0.65556 N/A N/A
6 Damian Michalski 2019-06-08 15:10 Vowpal Wabbit logistic + ngram vowpal-wabbit 0.81853 0.66535 0.81529 0.66585
9 Damian Michalski 2019-06-08 14:01 Vowpal Wabbit logistic + ngram vowpal-wabbit N/A N/A 0.80375 0.65156
45 Joanna P. (416422) 2019-06-04 22:16 solution 2e naive-bayes multinomial self-made 0.78276 0.59076 0.78242 0.59054
35 Maksym Krawczyk 2019-06-04 11:04 naive bayes - fine-tuned naive-bayes multinomial self-made probabilities 0.78569 0.59475 0.78404 0.59348
80 tomfur 2019-06-02 10:46 naiwny naive-bayes self-made 0.50088 0.38470 0.49733 0.38264
102 Anna D. 2019-06-02 08:33 Tweeter - self-made z zajęć 0.21735 0.00000 0.21725 0.00000
101 Katarzyna Owczarek 2019-06-01 20:46 zadanie z twitterem naive-bayes self-made 0.21735 0.00000 0.21725 0.00000
100 KJaron 2019-06-01 20:26 naive bayes ready made naive-bayes ready-made 0.78353 0.00000 0.78146 0.00000
99 Maciej 2019-06-01 17:34 my solution naive-bayes self-made N/A N/A 0.49742 0.00000
98 Jakub Sawczuk 2019-06-01 16:51 bayes gotowy naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
113 Jakub Sawczuk 2019-06-01 16:29 rozwiazanie bayesa naive-bayes ready-made N/A N/A N/A N/A
97 Monika 2019-06-01 14:35 naive bayes naive-bayes self-made 0.21735 0.00000 0.21725 0.00000
43 [anonymised] 2019-06-01 13:47 Solution naive-bayes self-made N/A N/A 0.78317 0.59102
47 [anonymised] 2019-06-01 13:06 Solution naive-bayes ready-made 0.78357 0.59313 0.77958 0.59009
33 Dariusz Ratajczak 2019-06-01 08:03 self-made_naive-bayes naive-bayes self-made 0.78653 0.59538 0.78696 0.59572
96 Meick Komisarek 2019-05-31 18:30 twutter naive-bayes ready-made 0.77592 0.00000 0.77479 0.00000
74 Wojciech Smolak 2019-05-30 14:17 fourth commit, naive bayes, ready-made naive-bayes ready-made 0.78373 0.53658 0.78125 0.53401
63 Wojciech Smolak 2019-05-30 13:36 third commit, naive bayes, self-made 0.78188 0.55732 0.78154 0.55690
61 Wojciech Smolak 2019-05-30 12:45 second commit, naive bayes, self-made naive-bayes self-made 0.78234 0.55788 0.78250 0.55808
95 Wojciech Smolak 2019-05-30 12:41 first commit, naive bayes, self-made 0.78234 0.00000 0.78250 0.00000
94 Natalia Orzechowska 2019-05-29 16:48 bayes ready made naive-bayes ready-made 0.52611 0.00000 0.51829 0.00000
50 Krzych 2019-05-28 19:50 naive-one naive-bayes self-made 0.77292 0.58441 0.77238 0.58406
54 Krzych 2019-05-28 19:48 naive-one naive-bayes self-made 0.77023 0.58268 0.77062 0.58294
40 Krzych 2019-05-28 19:35 naive-one naive-bayes self-made 0.78569 0.59266 0.78404 0.59159
57 Krzych 2019-05-28 19:29 naive-one naive-bayes self-made 0.77196 0.57701 0.77312 0.57758
82 Krzych 2019-05-28 19:22 naive-one naive-bayes self-made 0.73835 0.29749 0.73950 0.29907
93 Natalia Orzechowska 2019-05-28 18:52 bayes self made naive-bayes self-made 0.78519 0.00000 0.78371 0.00000
112 Natalia Orzechowska 2019-05-28 18:29 bayes self 0.00000 N/A 0.00000 N/A
7 Stanislaw-Golebiewski 2019-05-28 17:59 vw with -nn --ngram and linear regression vowpal-wabbit 0.81676 0.66360 0.81492 0.66244
13 Stanislaw-Golebiewski 2019-05-28 17:52 vm with -nn and linear regression 0.80130 0.65116 0.79838 0.64671
12 Stanislaw-Golebiewski 2019-05-28 17:48 vm with -nn and linear regression 0.80380 0.65082 0.80046 0.64857
92 Krzych 2019-05-27 22:36 naive-one naive-bayes self-made 0.78223 0.00000 0.78179 0.00000
46 Michał Groszewski 2019-05-27 17:25 Naive bayes - selfmade2 naive-bayes self-made 0.78238 0.59067 0.78150 0.59042
91 Michał Groszewski 2019-05-26 15:42 Naive Bayes - ready made naive-bayes ready-made 0.77592 0.00000 0.77479 0.00000
58 Gabi 2019-05-26 13:40 naive bayes with bpe naive-bayes bpe 0.76692 0.57771 0.76496 0.57607
90 Damian 2019-05-24 09:09 NB sig 3 naive-bayes self-made 0.76573 0.00000 0.76638 0.00000
55 Cezary Pukownik 2019-05-21 11:27 naive-bayes-selfmade naive-bayes self-made 0.76707 0.58113 0.76867 0.58223
89 Adam Cencek 2019-05-19 12:49 Bernoulii Bayes naive-bayes ready-made 0.78230 0.00000 0.78512 0.00000
88 Agnieszka Buszta 2019-05-19 12:11 tweet naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
41 Cezary Pukownik 2019-05-19 11:35 naive-bayes-readymade naive-bayes ready-made 0.78373 0.59324 0.78125 0.59135
87 Sara Woźniak 2019-05-19 11:31 tweety naive-bayes ready-made 0.78269 0.00000 0.78267 0.00000
76 Paulina Lester 2019-05-18 02:42 naive-bayes multino naive-bayes multinomial self-made 0.78561 0.50533 0.78675 0.50703
69 Marcin Szczepański 2019-05-17 19:03 Most positives and most negatives words naive-bayes multinomial 0.76819 0.54617 0.78275 0.54941
75 PioBec 2019-05-13 15:20 test-A naive-bayes multinomial self-made 0.76115 0.53251 0.75754 0.52830
51 Stefania Pikus 2019-05-08 12:06 naive bayes naive-bayes multinomial self-made 0.78588 0.58386 0.78442 0.58313
34 Stanislaw-Golebiewski 2019-05-08 10:43 hackaton naive-bayes multinomial self-made 0.78265 0.59347 0.78275 0.59402
44 Stanislaw-Golebiewski 2019-05-08 10:28 hackaton 0.78265 0.59069 0.78275 0.59075
71 mario 2019-05-07 21:27 lower() naive-bayes multinomial self-made N/A N/A 0.76379 0.54520
73 Adrian Witczak 2019-05-07 19:18 solution naive-bayes multinomial self-made 0.76857 0.54626 0.76362 0.54516
67 Maksym Krawczyk 2019-05-07 16:54 simple NB naive-bayes multinomial self-made 0.78569 0.55006 0.78404 0.54970
72 Bogusz 2019-05-07 16:51 naive bayes - lowercase naive-bayes multinomial self-made 0.76811 0.54616 0.76362 0.54516
68 Marcin Szczepański 2019-05-07 16:40 Naive bayes naive-bayes multinomial self-made 0.78265 0.54939 0.78275 0.54941
70 Bogusz 2019-05-07 16:38 classes naive-bayes multinomial self-made 0.78215 0.00000 0.78146 0.54912
59 Olga Kwaśniewska 2019-05-07 12:37 Naive Bayes naive-bayes multinomial self-made 0.76796 0.56835 0.76388 0.56434
81 Filip Hałoń 2019-05-07 12:13 Bayes zajęcia naive-bayes multinomial self-made 0.78588 0.37011 0.78442 0.36763
86 Joanna P. (416422) 2019-05-07 12:02 solution naive-bayes multinomial self-made 0.78276 0.00000 0.78242 0.00000
37 [anonymised] 2019-05-06 19:50 implement multinomial Naive Bayes ... on labs naive-bayes multinomial self-made 0.78276 0.59250 0.78242 0.59224
39 [anonymised] 2019-05-06 19:43 implement multinomial Naive Bayes ... on labs naive-bayes multinomial self-made 0.78276 0.59197 0.78242 0.59169
38 [anonymised] 2019-05-06 18:41 bayes with probabilities naive-bayes multinomial self-made 0.78276 0.59197 0.78242 0.59169
111 PioBec 2019-05-06 17:03 poprawa Likelyhood naive-bayes multinomial self-made 0.76115 0.53251 N/A N/A
110 PioBec 2019-05-06 16:58 Zajecia 0.76115 0.00000 N/A N/A
85 Stefania Pikus 2019-05-06 16:55 bayes naive-bayes multinomial self-made 0.78276 0.00000 0.78242 0.00000
64 Gabi 2019-05-06 16:51 simple bayes with probs naive-bayes multinomial self-made 0.78353 0.55935 0.78154 0.55690
66 Mateusz Hinc 2019-05-06 16:44 bayes first try naive-bayes multinomial self-made 0.78311 0.55883 0.77975 0.55472
60 Artur Nowakowski 2019-05-06 16:44 naive bayes naive-bayes multinomial self-made 0.78473 0.56081 0.78271 0.55833
65 Mateusz Hinc 2019-05-06 16:42 bayes first try 0.78311 0.00000 0.77975 0.55472
62 Artur Nowakowski 2019-05-06 16:40 naive bayes 0.78357 0.55939 0.78217 0.55767
48 Yurkee 2019-05-06 16:39 naive bayes ZAJECIA naive-bayes multinomial self-made 0.78569 0.58986 0.78404 0.58889
84 Yurkee 2019-05-06 16:37 naive bayes ZAJECIA 0.78569 0.00000 0.78404 0.00000
21 Kamig 2019-03-21 12:23 tcn 0.79507 0.62366 0.79421 0.62513
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
27 [anonymised] 2019-01-27 22:20 Multinomial NB TfidfVectorizer without english stopwords 0.77330 0.60146 0.77242 0.60333
22 [anonymised] 2019-01-27 22:17 Bernoulli NB TfidfVectorizer without english stopwords 0.78649 0.60804 0.78729 0.60838
11 [anonymised] 2019-01-27 22:12 Logistic Regression TfidfVectorizer without english stopwords 0.80691 0.64968 0.80662 0.64890
26 [anonymised] 2019-01-27 22:05 Bernoulli NB TfidfVectorizer with removed numbers 0.76992 0.60318 0.77167 0.60449
19 [anonymised] 2019-01-27 22:01 Logistic Regression TfidfVectorizer with removed numbers 0.78276 0.62819 0.78154 0.62815
31 [anonymised] 2019-01-27 21:35 Multinomial NB TfidfVectorizer without removed punctation 0.76319 0.59878 0.76512 0.59979
17 [anonymised] 2019-01-27 21:34 Logistic Regression TfidfVectorizer without removed punctation 0.78507 0.63037 0.78462 0.62967
30 [anonymised] 2019-01-27 21:31 Bernoulli NB TfidfVectorizer without removed punctation 0.76938 0.60134 0.77129 0.60057
23 [anonymised] 2019-01-27 21:24 Bernoulli NB TfidfVectorizer(min_df=5, max_df=0.8) 0.76996 0.60453 0.77242 0.60594
20 [anonymised] 2019-01-27 21:22 Logistic Regression TfidfVectorizer(min_df=5, max_df=0.8) 0.78000 0.62732 0.77983 0.62726
29 [anonymised] 2019-01-27 21:21 Multinomial NB TfidfVectorizer(min_df=5, max_df=0.8) 0.76269 0.59937 0.76350 0.60112
32 [anonymised] 2019-01-27 21:15 Multinomial NB TfidfVectorizer 0.76088 0.59746 0.76208 0.59892
18 [anonymised] 2019-01-27 21:13 Logistic Regression TfidfVectorizer 0.78253 0.62812 0.78150 0.62845
28 [anonymised] 2019-01-27 21:11 Bernoulli NB TfidfVectorizer 0.77046 0.60293 0.77204 0.60332
83 [anonymised] 2019-01-27 21:08 linear_SVG TfidfVectorizer 0.77611 0.00000 0.77300 0.00000
14 [anonymised] 2019-01-08 01:38 LogisticRegression TfidfVectorizer without @-mentions 0.80295 0.64782 0.80388 0.64628
15 [anonymised] 2019-01-08 01:30 LogisticRegression TfidfVectorizer without @-mentions, URLs 0.80284 0.64713 0.80308 0.64548
16 [anonymised] 2019-01-08 01:25 LogisticRegression TfidfVectorizer without HTML tags, @-mentions, hash-tags, URLs, ... N/A N/A 0.80229 0.64400
10 [anonymised] 2019-01-07 23:58 LogisticRegression TfidfVectorizer 0.80665 0.65007 0.80796 0.64925
25 [anonymised] 2019-01-07 23:45 BernoulliNB TfidfVectorizer 0.78515 0.60497 0.78612 0.60469
79 [anonymised] 2019-01-07 23:38 NN: 10 Relu, 1 sigmoid - countvec 0.50108 0.49999 0.49750 0.49999
78 [anonymised] 2019-01-07 23:31 NN: 10 Relu, 1 sigmoid - tfidf 0.50104 0.50000 0.49825 0.50000
24 [anonymised] 2019-01-07 18:27 BernoulliNB 0.78515 0.60497 0.78612 0.60469
49 [anonymised] 2019-01-07 17:49 MultinomialNB 0.77815 0.58650 0.77825 0.58509
77 p/tlen 2018-12-18 06:57 Null model stupid null-model 0.49912 0.50000 0.50267 0.50000