Datasets\nIMDb Large Movie Review Dataset - This dataset contains movie reviews along with their associated binary sentiment polarity labels. It is intended to serve as a benchmark for sentiment classification.\nLink: http://ai.stanford.edu/~amaas/data/sentiment/\n\nSentiment140 - This dataset contains 1.6 million tweets extracted using the Twitter API. The tweets have been annotated (0 = negative, 2 = neutral, 4 = positive) and they can be used to detect sentiment.\nLink: http://help.sentiment140.com/for-students/\n\nYelp Reviews - An open dataset released by Yelp for learning purposes. It consists of millions of reviews with star ratings that can be used for sentiment analysis.\nLink: https://www.yelp.com/dataset\n\nAmazon Reviews for Sentiment Analysis - This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. The dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).\nLink: http://jmcauley.ucsd.edu/data/amazon/\n\nTwitter US Airline Sentiment - A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons.\nLink: https://www.kaggle.com/crowdflower/twitter-airline-sentiment\n\nStanford Sentiment Treebank - This dataset includes fine-grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences.\nLink: ctrader vs mt4\n