Update README.md

README.md

@@ -11,8 +11,8 @@ whether they were upset, neutral, or satisfied with the trip and the airline's s
 
 ## Dataset Details
 
-The dataset is a smaller version of the original dataset [This data originally came from Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone)
-The original Twitter data was scraped from February 2015, and contributors were asked first to classify positive, negative[, and neutral tweets,
+The dataset is a smaller version of the original datase. This data originally came from [Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone)
+ The original Twitter data was scraped from February 2015, and contributors were asked first to classify positive, negative, and neutral tweets,
 followed by categorizing negative reasons (such as "late flight" or "rude service").
 This version contains whether the sentiment of the tweets in this set was positive (16%), neutral (21%), or negative (63%) for six US airlines.
 
@@ -26,7 +26,8 @@ The dataset consists of a data frame with the following columns:
 - text
 
 {
-"airline_sentiment": "negative[0]",
+"airline_sentiment": "negative [0]",
+
 "text":"virginamerica why are your first fares in may over three times more than other carriers when all seats are available to select.",
 }