Content Combined texts for sentiment analysis - sources are: - [Sentiment Labelled Sentences Data Set](https://www.kaggle.com/marklvl/sentiment-labelled-sentences-data-set/data) - [Primary Emotions of Statements](https://data.world/crowdflower/primary-emotions-of-statements) - [Brands and Product Emotions](https://data.world/crowdflower/brands-and-product-emotions) - [SMILE Twitter Emotion dataset](https://figshare.com/articles/smile_annotations_final_csv/3187909) - [Emotion Mining Toolkit (EMTk)](https://zenodo.org/record/2575509#.XlSdCi1L01J) - [Detecting hatred tweets](https://www.kaggle.com/arkhoshghalb/twitter-sentiment-analysis-hatred-speech) - [Sentiment140 dataset with 1.6 million tweets](https://www.kaggle.com/kazanova/sentiment140) - [Amazon Reviews for Sentiment Analysis](https://www.kaggle.com/bittlingmayer/amazonreviews) - [Sentiment Analysis in Text](https://data.world/crowdflower/sentiment-analysis-in-text) - [EmoBank](https://github.com/JULIELab/EmoBank) - [[WASSA-2017 Shared Task on Emotion Intensity](http://saifmohammad.com/WebPages/EmotionIntensity-SharedTask.html)]() - [The Valence and Arousal Facebook Posts](http://wwbp.org/downloads/public_data/dataset-fb-valence-arousal-anon.csv) - [Affect data](http://people.rc.rit.edu/~coagla/affectdata/index.html) - [IMDb](http://www.cs.cornell.edu/people/pabo/movie%2Dreview%2Ddata/) - [SST-2](https://www.kaggle.com/atulanandjha/stanford-sentiment-treebank-v2-sst2) - [SST-5](https://github.com/prrao87/fine-grained-sentiment) - [Yelp](https://www.yelp.com/dataset) Usage import pickle with open('sentiment\_data.pkl', 'rb') as f: data = pickle.load(f) test = data[data.Training\_evaluation\_split.eq('Evaluation')] train = data[data.Training\_evaluation\_split.eq('Training')] test\_x = test.text test\_y = test.Polarity train\_x = train.text train\_y = train.Polarity