Airlines Sentiment Classification
Budget: $10 – $30 USD
Performing analysis on customer feedback, such as opinions in survey responses and social media conversations, allows brands to listen attentively to their customers, and tailor products and services to meet their needs. However, all the opiniated data from the Twitter is in the form of text which is unstructured.
This unstructured data is hard to analyze, understand, sort through, and also time-consuming and expensive. Sentiment analysis, however, helps businesses make sense of all this unstructured text by automatically understanding, processing, and tagging it.
Objective of this project is to perform sentiment analysis on the tweets of six US Airlines. The scrapped tweets contain positive, negative, or neutral sentiments about the airline from their respective customers. The task is to analyze how travelers in February 2015 expressed their feelings on Twitter about six major US airlines.
This unstructured data is hard to analyze, understand, sort through, and also time-consuming and expensive. Sentiment analysis, however, helps businesses make sense of all this unstructured text by automatically understanding, processing, and tagging it.
Objective of this project is to perform sentiment analysis on the tweets of six US Airlines. The scrapped tweets contain positive, negative, or neutral sentiments about the airline from their respective customers. The task is to analyze how travelers in February 2015 expressed their feelings on Twitter about six major US airlines.