Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing

• Autorzy: Hazarika Ditiman , Konwar Gopal , Deb Shuvam , Bora Dibya Jyoti

Identyfikatory

DOI

10.15439/2020KM20

• Konferencja: International Conference on Research in Management & Technovation (05-06.12.2020 ; Nagpur, Indie)
• Języki publikacji: EN

Abstrakty

EN

The Internet has become an innovative platform regarding online learning, exchanging content, sharing views. In this paper, we will use Twitter as our social networking platform. Sentiment analysis on Twitter is based on opinion mining on posts to obtain the user's point of view. The leading goal deals with how opinion mining techniques can be accessed to analyze some of the tweets in many reports involving various types of tweet languages on Twitter and classify its polarity. Practical implication shows that the proposed machine learning classifiers are efficient and highly accurate.

Słowa kluczowe

EN

sentiments; opinion; polarity; classification techniques; TextBlob

PL

opinia; biegunowość; techniki klasyfikacji; TextBlob

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 24
• Strony: 63--67
• Opis fizyczny: Bibliogr. 6 poz., rys.

Twórcy

autor

Hazarika Ditiman

• Student, School of Computing Sciences(IT), The Assam Kaziranga University, Jorhat, Assam, India

autor

Konwar Gopal

• Student, School of Computing Sciences(IT), The Assam Kaziranga University, Jorhat, Assam, India

autor

Deb Shuvam

• Student, School of Computing Sciences(IT), The Assam Kaziranga University, Jorhat, Assam, India

autor

Bora Dibya Jyoti

• School of Computing Sciences(IT), The Assam Kaziranga University Jorhat, Assam, India

Bibliografia

• 1. https://www.lexalytics.com/news/press-releases/lexalytics-unveils-sentiment-analysis-of-emoticons-acronyms.
• 2. https://www.digitalvidya.com/blog/twitter-sentiment-analysis-introduction-and-techniques
• 3. https://www.quora.com/What-is- Twitter-sentiment-analysis
• 4. Kiruthika, Sanjana, & Giri, “ Sentiment analysis of Twitter Data”, International Journal of Innovation in Engineering and Technology, Vol No.6, April 2016
• 5. Mitali Desai, Mayuri A. Mehta, “Techniques for Sentiment Analysis of Twitter Data: A Comprehensive Survey”, International Conference on Computing, Communication, and Automation (ICCCA2016)
• 6. Sanjeev Kumar Sharma, “Sentiment Analysis: An analysis on its past, present and future scope”, International Journal of advanced research in Science and Engineering, Vol.No.6, Issue No.07, July 2017.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).