83 datasets found

The dataset is a corpus of movie reviews and anonymous imageboard messages annotated with consideration of containing or not state of aggression.

The Yelp Reviews Polarity dataset contains 560k and 38k (in training and dev portion respectively) customer reviews in English from Yelp.

Semeval-2016 Task 6: Detecting stance in tweets.

The dataset used for sentiment analysis of climate change-related tweets.

The Rotten Tomatoes dataset has 5331 positive and 5331 negative review sentences.

The SST2 dataset has 6920/872/1821 example sentences in the train/dev/test sets. The task is binary classification into positive/negative sentiment. The IMDB dataset has...

The dataset used for training a text classifier to learn the polarity of hotel reviews.

The Memotion Dataset 7K is a collection of 7000 memes with associated metadata.

The dataset used in the paper is a collection of unstructured text data from social networks, news sites, and forums.

The Polarity dataset contains text documents with sentiment labels.

The IMDb review dataset is used for positive generation task.

Annotating expressions of opinions and emotions in language

The dataset used in the paper is the Stanford Sentiment Treebank (SST) dataset, which contains standard train/dev/test sets and two subtasks: binary sentence classification or...

The Jester dataset contains joke ratings in a continuous scale from 1 to 10 for 100 jokes from a total of 73421 users.

The gisette dataset is a collection of 20,000 text documents, each containing a single sentence.

The epsilon dataset is a collection of 50,000 text documents, each containing a single sentence.

The dataset used in this study is a collection of Twitter data, containing all relevant tweets published for each stock.

The dataset used for text classification tasks, including SST-1, SST-2, SUBJ, and IMDB.

The dataset used in the paper is a real-world dataset for sentiment analysis.

The Twitter Dataset is a collection of tweets annotated with Plutchik's emotions, consisting of tweets in three different languages: English, Dutch, and German.