# Twitter Sentiment Analysis

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

Git repo URL: `git://gonito.net/twitter-sentiment-analysis` / Branch: ` master`

Run `git clone --single-branch git://gonito.net/twitter-sentiment-analysis -b master` to get the challenge data

Browse at `https://gonito.net/gitlist/twitter-sentiment-analysis.git/master`

## Leaderboard

# | submitter | when | ver. | description | test-A Accuracy | test-A Likelihood | × | |
---|---|---|---|---|---|---|---|---|

1 | kubapok | 2020-05-20 08:31 | 1.0.0 | roberta large finetunned 1 epoch | 0.89950 | 0.77317 | 2 | |

2 | [anonymized] | 2019-02-15 12:33 | 1.0.0 | ulmfit-textbugger (adversarials created on 30% of trainset) | 0.86008 | 0.72071 | 9 | |

3 | [anonymized] | 2019-08-23 12:07 | 1.0.0 | Wynik po 100000 cech ze stopwords, TfidfVectorizer, ngram=(1,3), LogisticRegression | 0.82467 | 0.67126 | 173 | |

4 | [anonymized] | 2019-06-11 15:26 | 1.0.0 | vw script added vowpal-wabbit | 0.81846 | 0.66975 | 5 | |

5 | [anonymized] | 2019-06-08 15:10 | 1.0.0 | Vowpal Wabbit logistic + ngram vowpal-wabbit | 0.81529 | 0.66585 | 7 | |

6 | [anonymized] | 2019-05-28 17:59 | 1.0.0 | vw with -nn --ngram and linear regression vowpal-wabbit | 0.81492 | 0.66244 | 5 | |

7 | [anonymized] | 2020-05-31 19:23 | 1.0.0 | v5.1 ready-made svm | 0.76850 | 0.61405 | 8 | |

8 | [anonymized] | 2020-05-29 10:46 | 1.0.0 | ISI-89, xgboost, ready-made, sklearn, dockerfile ready-made xgboost | 0.75775 | 0.59717 | 1 | |

9 | [anonymized] | 2019-06-01 08:03 | 1.0.0 | self-made_naive-bayes naive-bayes self-made | 0.78696 | 0.59572 | 2 | |

10 | [anonymized] | 2019-06-04 11:04 | 1.0.0 | naive bayes - fine-tuned naive-bayes multinomial self-made probabilities | 0.78404 | 0.59348 | 3 | |

11 | [anonymized] | 2019-05-06 19:50 | 1.0.0 | implement multinomial Naive Bayes ... on labs naive-bayes multinomial self-made | 0.78242 | 0.59224 | 2 | |

12 | [anonymized] | 2019-05-06 18:41 | 1.0.0 | bayes with probabilities naive-bayes multinomial self-made | 0.78242 | 0.59169 | 1 | |

13 | [anonymized] | 2019-05-28 19:35 | 1.0.0 | naive-one naive-bayes self-made | 0.78404 | 0.59159 | 6 | |

14 | [anonymized] | 2019-06-17 11:46 | 1.0.0 | readymade_naive-bayes.py naive-bayes ready-made | 0.78125 | 0.59135 | 7 | |

15 | [anonymized] | 2019-05-19 11:35 | 1.0.0 | naive-bayes-readymade naive-bayes ready-made | 0.78125 | 0.59135 | 5 | |

16 | [anonymized] | 2019-06-01 13:47 | 1.0.0 | Solution naive-bayes self-made | 0.78317 | 0.59102 | 2 | |

17 | [anonymized] | 2019-06-04 22:16 | 1.0.0 | solution 2e naive-bayes multinomial self-made | 0.78242 | 0.59054 | 2 | |

18 | [anonymized] | 2019-05-27 17:25 | 1.0.0 | Naive bayes - selfmade2 naive-bayes self-made | 0.78150 | 0.59042 | 7 | |

19 | [anonymized] | 2019-05-06 16:39 | 1.0.0 | naive bayes ZAJECIA naive-bayes multinomial self-made | 0.78404 | 0.58889 | 2 | |

20 | [anonymized] | 2020-05-06 14:03 | 1.0.0 | ISI-048 probabilities with kenlm kenlm | 0.77812 | 0.58818 | 1 | |

21 | [anonymized] | 2020-05-11 13:45 | 1.0.0 | KenLM v1 kenlm | 0.77804 | 0.58771 | 2 | |

22 | [anonymized] | 2019-05-08 12:06 | 1.0.0 | naive bayes naive-bayes multinomial self-made | 0.78442 | 0.58313 | 2 | |

23 | [anonymized] | 2019-06-09 11:22 | 1.0.0 | Zajecia Bayes final4 naive-bayes multinomial self-made | 0.78442 | 0.58313 | 4 | |

24 | [anonymized] | 2019-05-26 13:40 | 1.0.0 | naive bayes with bpe naive-bayes bpe | 0.76496 | 0.57607 | 5 | |

25 | [anonymized] | 2020-06-12 21:29 | 1.0.0 | lstm solution lstm | 0.76362 | 0.57162 | 1 | |

26 | [anonymized] | 2020-05-18 15:31 | 1.0.0 | isrtlm first try irstlm | 0.75750 | 0.56999 | 1 | |

27 | [anonymized] | 2019-05-07 12:37 | 1.0.0 | Naive Bayes naive-bayes multinomial self-made | 0.76388 | 0.56434 | 1 | |

28 | Artur Nowakowski | 2019-05-06 16:44 | 1.0.0 | naive bayes naive-bayes multinomial self-made | 0.78271 | 0.55833 | 2 | |

29 | [anonymized] | 2019-05-30 12:45 | 1.0.0 | second commit, naive bayes, self-made naive-bayes self-made | 0.78250 | 0.55808 | 4 | |

30 | [anonymized] | 2019-05-06 16:44 | 1.0.0 | bayes first try naive-bayes multinomial self-made | 0.77975 | 0.55472 | 2 | |

31 | [anonymized] | 2019-05-17 19:03 | 1.0.0 | Most positives and most negatives words naive-bayes multinomial | 0.78275 | 0.54941 | 5 | |

32 | [anonymized] | 2019-05-07 16:38 | 1.0.0 | classes naive-bayes multinomial self-made | 0.78146 | 0.54912 | 2 | |

33 | [anonymized] | 2019-05-07 21:27 | 1.0.0 | lower() naive-bayes multinomial self-made | 0.76379 | 0.54520 | 1 | |

34 | [anonymized] | 2019-05-07 19:18 | 1.0.0 | solution naive-bayes multinomial self-made | 0.76362 | 0.54516 | 6 | |

35 | [anonymized] | 2019-05-13 15:20 | 1.0.0 | test-A naive-bayes multinomial self-made | 0.75754 | 0.52830 | 6 | |

36 | [anonymized] | 2020-05-11 16:04 | 1.0.0 | kenlm#15 kenlm | 0.65150 | 0.52102 | 15 | |

37 | [anonymized] | 2019-05-18 02:42 | 1.0.0 | naive-bayes multino naive-bayes multinomial self-made | 0.78675 | 0.50703 | 1 | |

38 | [anonymized] | 2020-05-25 19:37 | 1.0.0 | IRSTLM v22 irstlm | 0.59567 | 0.50327 | 23 | |

39 | p/tlen | 2018-12-18 06:57 | 1.0.0 | Null model stupid null-model | 0.50267 | 0.50000 | 1 | |

40 | [anonymized] | 2019-06-02 10:46 | 1.0.0 | naiwny naive-bayes self-made | 0.49733 | 0.38264 | 5 | |

41 | [anonymized] | 2019-05-07 12:13 | 1.0.0 | Bayes zajęcia naive-bayes multinomial self-made | 0.78442 | 0.36763 | 1 | |

42 | [anonymized] | 2019-06-01 14:35 | 1.0.0 | naive bayes naive-bayes self-made | 0.21725 | 0.00000 | 1 | |

43 | [anonymized] | 2019-06-01 20:26 | 1.0.0 | naive bayes ready made naive-bayes ready-made | 0.78146 | 0.00000 | 1 | |

44 | [anonymized] | 2019-06-01 17:34 | 1.0.0 | my solution naive-bayes self-made | 0.49742 | 0.00000 | 1 | |

45 | [anonymized] | 2019-06-16 09:15 | 1.0.0 | dk tw readymade naive-bayes self-made | 0.50100 | 0.00000 | 1 | |

46 | [anonymized] | 2019-05-24 09:09 | 1.0.0 | NB sig 3 naive-bayes self-made | 0.76638 | 0.00000 | 31 | |

47 | [anonymized] | 2019-06-16 07:44 | 1.0.0 | twitter ready made naive-bayes ready-made | 0.76962 | 0.00000 | 2 | |

48 | [anonymized] | 2019-06-16 09:05 | 1.0.0 | Achievement07 naive-bayes ready-made | 0.78267 | 0.00000 | 1 | |

49 | [anonymized] | 2019-05-19 12:49 | 1.0.0 | Bernoulii Bayes naive-bayes ready-made | 0.78512 | 0.00000 | 1 | |

50 | [anonymized] | 2019-05-31 18:30 | 1.0.0 | twutter naive-bayes ready-made | 0.77479 | 0.00000 | 2 | |

51 | [anonymized] | 2019-05-29 16:48 | 1.0.0 | bayes ready made naive-bayes ready-made | 0.51829 | 0.00000 | 3 | |

52 | [anonymized] | 2019-06-01 20:46 | 1.0.0 | zadanie z twitterem naive-bayes self-made | 0.21725 | 0.00000 | 2 | |

53 | [anonymized] | 2019-06-01 16:51 | 1.0.0 | bayes gotowy naive-bayes ready-made | 0.78267 | 0.00000 | 3 | |

54 | [anonymized] | 2019-05-19 11:31 | 1.0.0 | tweety naive-bayes ready-made | 0.78267 | 0.00000 | 2 | |

55 | [anonymized] | 2019-05-19 12:11 | 1.0.0 | tweet naive-bayes ready-made | 0.78267 | 0.00000 | 1 | |

56 | [anonymized] | 2019-06-02 08:33 | 1.0.0 | Tweeter - self-made z zajęć | 0.21725 | 0.00000 | 2 | |

57 | [anonymized] | 2019-06-15 23:46 | 1.0.0 | Twitter Bayes naive-bayes ready-made | 0.78371 | 0.00000 | 1 | |

58 | [anonymized] | 2020-06-21 16:20 | 1.0.0 | roberta pretrained roberta | 0.84529 | 0.00000 | 1 |