Airlines Twitter Sentiment Analysis Using EDA AND ML
Keywords:
Pertaining, Gauge, Tokenizing, SentimentAbstract
Today, social media is a part of everyone, so there are a lot of user views available. A sentiment analysis technique called "airlines sentiment analysis" may be used to examine the issues or views of all significant U.S. airlines. Twitter data from various years was scraped in order to understand the customer's voice. Contributors were requested to first categorise positive, neutral, and negative tweets before classifying negative tweets like delayed flights or rude service. The collection of sentiment tweets for six US airlines, whether they were positive, negative, or neutral, is included in the CSV file. Social media sites like Facebook, Twitter, etc. are being used more frequently by people worldwide. Using social media, they exchange ideas, views, and information. More attention was paid to these social issues by the business.
References
- X. "Mining airline passenger sentiment from Twitter data," Journal of Air Transport Management, pp. 10-17, 2017.
- Wang, "Analysis of airline user-generated content on social media: Sentiment, topics, and insights," Journal of Air Transport Management, pp. 49-61, 2018.
- Nair and S. , "A sentiment analysis of tweets related to Indian airlines using machine learning techniques," Journal of Air Transport Management, pp. 101-110, 2017.
- Lee, "The influence of Twitter sentiment on customer loyalty in the airline industry," Journal of Air Transport Management, pp. 135-143, 2018.
- Alharbi and A. , "Analysis of customer sentiment towards Saudi Arabian airlines using sentiment analysis," Journal of Airline and Airport Management, pp. 1-18, 2019.
- Guo, B. and J. , "Chinese airline passenger satisfaction and loyalty: An analysis of online reviews," Journal of Air Transport Management, pp. 99-108.
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