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FILTERING AIRLINE SENTIMENT FROM TWITTER TWEETS USING NATURAL LANGUAGE PROCESSING

  • Unique Paper ID: 167584
  • Volume: 11
  • Issue: 3
  • PageNo: 1714-1719
  • Abstract:
  • The competitive airline sector has experienced rapid growth over the past two decades. Effective data collection is crucial for gathering consumer feedback and conducting various forms of analysis within this dynamic industry. One such analysis is sentiment analysis, which involves extracting sentiments to discern attitudes and emotions associated with the provided text or data. Our Project deals with sentiment analysis techniques applied to the airline industry. Sentiment analysis employs classification approaches using machine learning techniques to identify positive and negative sentiments within text-driven databases. Additionally, word clouds and bar graphs are utilized to further elucidate the reasons behind negative comments. In this study, sentiment analysis is conducted on the Airline Reviews dataset. To assess the performance of sentiment analysis, various machine learning algorithms are employed, including Naive Bayes, Support Vector Machine, and Decision Tree. Each approach yields distinct results, highlighting the importance of selecting appropriate algorithms for accurate sentiment analysis within the airline industry.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 1714-1719

FILTERING AIRLINE SENTIMENT FROM TWITTER TWEETS USING NATURAL LANGUAGE PROCESSING

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