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An Excel with ~1600 Comments:
Each comment was written as a response to a Facebook Status published by an Israeli MK, During 2015-2016, randomly selected. Each comment has some 80 extracted features, and 9 manually tagged/classified features regarding the sentiment in the comment. Link here.
An Excel with Feature Description:
As mentioned above, each Comment was tagged for 9 different Attributes/features/dependent variables. The Codebook describes the feature and classifcation guidelines.
Link here.
A link to ~5.3M Comments, unclassified. a txt file, one comment per row. Link here.
A More detailed description on the data collection and sampling process, and a discussion on some of its features can be found here (Chapter 2 and onwards).
Goals:
Build interesting and reliable predictive models.
Any result will be interesting, but a focus on good classification of comment sentiment will be the most useful for current efforts.
The text was updated successfully, but these errors were encountered:
Materials:
An Excel with ~1600 Comments:
Each comment was written as a response to a Facebook Status published by an Israeli MK, During 2015-2016, randomly selected. Each comment has some 80 extracted features, and 9 manually tagged/classified features regarding the sentiment in the comment. Link here.
An Excel with Feature Description:
As mentioned above, each Comment was tagged for 9 different Attributes/features/dependent variables. The Codebook describes the feature and classifcation guidelines.
Link here.
A link to ~5.3M Comments, unclassified. a txt file, one comment per row. Link here.
A More detailed description on the data collection and sampling process, and a discussion on some of its features can be found here (Chapter 2 and onwards).
Goals:
The text was updated successfully, but these errors were encountered: