s452101/twitter-sentiment-analysis
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Jakub 978f655319 ISI-89, xgboost, ready-made, sklearn, dockerfile
2020-05-29 12:44:36 +02:00
dev-0 ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
test-A ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
train ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
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config.txt ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
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README.md ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
requirements.txt ISI-89, xgboost, ready-made, sklearn 2020-05-29 11:37:33 +02:00
run.sh ISI-89, xgboost, ready-made, sklearn, dockerfile 2020-05-29 12:44:36 +02:00
solution.py ISI-89, xgboost, ready-made, sklearn, dockerfile 2020-05-29 12:44:36 +02:00

README.md

Twitter Sentiment Analysis

Guess the sentiment for texts in English.

Classes

  • 1 — positive sentiment
  • 0 — negative sentiment

Directory structure

  • README.md — this file
  • config.txt — configuration file
  • train/ — directory with training data
  • train/train.tsv — train set, (text - 1st column, sentiment - 2nd column) a text fragment in the second one
  • dev-0/ — directory with dev (test) data
  • dev-0/in.tsv — input data for the dev set (text fragments)
  • dev-0/expected.tsv — expected (reference) data for the dev set
  • test-A — directory with test data
  • test-A/in.tsv — input data for the test set (text fragments)
  • test-A/expected.tsv — expected (reference) data for the test set (hidden)