SemEval-2016

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• SemEval-2016 Task 4: Sentiment Analysis on Twitter *

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•           TRAINING + DEV DATA                  *
  

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• http://alt.qcri.org/semeval2016/task4/ *
• [email protected] *

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TRAINING + DEV dataset for SemEval-2016 Task 4

Version 1.0: October 15, 2015

Task organizers:

• Preslav Nakov, Qatar Computing Research Institute, HBKU
• Alan Ritter, The Ohio State University
• Sara Rosenthal, Columbia University
• Fabrizio Sebastiani, Qatar Computing Research Institute, HBKU
• Veselin Stoyanov, Facebook

NOTES

1. Please note that by downloading the Twitter data you agree to abide by the Twitter terms of service (https://twitter.com/tos), and in particular you agree not to redistribute the data and to delete tweets that are marked deleted in the future.

2. The distribution consists of a set of Twitter status IDs with annotations for Subtasks A, B, C, D, and E: topic polarity and trends toward a topic. There are exactly 100 tweets provided per topic and a total of 100 topics. You should use the downloading script to obtain the corresponding tweets: https://github.com/aritter/twitter_download

3. The "neutral" label in the annotations stands for objective_OR_neutral.

FILES

data/train/src/100_topics_100_tweets.topic-two-point.subtask-BD.train.txt -- training input for subtasks B and D data/train/src/100_topics_100_tweets.topic-five-point.subtask-CE.train.txt -- training input for subtasks C and E

data/dev/src/100_topics_100_tweets.topic-two-point.subtask-BD.dev.txt -- dev input for subtasks B and D data/dev/src/100_topics_100_tweets.topic-five-point.subtask-CE.dev.txt -- dev input for subtasks C and E

INPUT DATA FORMAT

-----------------------SUBTASK A-----------------------------------------

The format for the training/dev file is as follows:

id<TAB>label

where "label" can be 'positive', 'neutral' or 'negative'.

-----------------------SUBTASKS B,D-------------------------------------- ** Task we might deal with.

The format for the training/dev file is as follows:

topic<TAB>id<TAB>label

where "label" can be 'positive' or 'negative' (note: no 'neutral'!).

-----------------------SUBTASKS C,E--------------------------------------

• Task we are dealing with.

The format for the training/dev file is as follows:

topic<TAB>id<TAB>label

where "label" can be -2, -1, 0, 1, or 2, corresponding to "strongly negative", "negative", "negative or neutral", "positive", and "strongly positive".

LICENSE

The accompanying dataset is released under a Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/).

CITATION

You can cite the folowing paper when referring to the dataset:

@InProceedings{Rosenthal-EtAl:2015:SemEval, author = {Sara Rosenthal and Alan Ritter and Veselin Stoyanov and Svetlana Kiritchenko and Saif Mohammad and Preslav Nakov}, title = {SemEval-2015 Task 10: Sentiment Analysis in Twitter}, booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)}, year = {2015}, publisher = {Association for Computational Linguistics}, }

USEFUL LINKS:

Google group: [email protected] SemEval-2016 Task 4 website: http://alt.qcri.org/semeval2016/task4/ SemEval-2016 website: http://alt.qcri.org/semeval2016/