The following link provides a variety of datasets that contain text sentences that have been extracted from reviews of products (from amazon.com), reviews of movies (from IMDB.com), and reviews of restaurants (from yelp.com): https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences.

Each sentence is labeled in terms of the sentiment that was expressed in the reviews. This sentiment is either positive or negative. For each website, there are 500 positive and 500 negative sentences, which means there are 3,000 labeled sentences in total. This data can be used to develop a sentiment classification deep networking model that can help us automatically classify a customer review as either positive or negative.

The following are some examples of negative reviews from IMDb that have been labeled as 0:

• A very, very, very slow-moving, aimless movie about a distressed, drifting young man
• Not sure who was more lost—the flat characters or the audience, nearly half of whom walked out
• Attempting artiness with black and white and clever camera angles, the movie disappointed—became even more ridiculous—as the acting was poor and the plot and lines almost non-existent
• Very little music or anything to speak of 

The following are some examples of positive reviews from IMDb that have been labeled as 1:

• The best scene in the movie was when Gerardo was trying to find a song that kept running through his head
• Saw the movie today and thought it was a good effort, good messages for kids
• Loved the casting of Jimmy Buffet as the science teacher
• And those baby owls were adorable
• The movie showed a lot of Florida at its best, made it look very appealing

The following are some examples of negative reviews from Amazon that are labeled as 0:

• So there is no way for me to plug it in here in the US unless I go by a converter
• Tied to charger for conversations lasting more than 45 minutes. MAJOR PROBLEMS!!
• I have to jiggle the plug to get it to line up right to get decent volume
• If you have several dozen or several hundred contacts, then imagine the fun of sending each of them one by one
• I advise EVERYONE DO NOT BE FOOLED!

The following are some examples of positive reviews from Amazon that are labeled as 1:

• Good case, Excellent value
• Great for the jawbone
• The mic is great
• If you are Razr owner...you must have this!
• And the sound quality is great