Search Results for "which"

* To cite this dataset refer to https://doi.org/10.31449/inf.v46i6.3340 * This sentiment dataset of Algerian dialect consists of 11760 comments (6111 positive/ 5649 negative comments)) collected from (Facebook, YouTube and Twitter) during Hirak 2019. * Comments concern the Algerian spoken language, written in Arabic and/or Latin characters and/or Arabizi, which could be either Modern Standard Arabic, French or local dialect. * Value ‘1’ is attributed for Positive review / value ‘0’ attributed for Negative review. * Due to the nature of this Dataset, some comments contain offensive language. This does not reflect author values, however the aim is to providing a resource to help in analysing positive and negative sentiments (that probably containing harmful content)...

...The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).