Abstract
With the use of web 2.0 technology, people often share their reviews, opinions, news, and information with others. They express their experiences regarding vacations, complain about movies, or rave about restaurants, and discuss various latest sports rumors. The speed and ease of communication have increased due to social media platforms. Consumer shares their opinions about products and services on consumer opinion portals such as Facebook, Instagram, Twitter, blogs, WhatsApp, Snapchat, LinkedIn, etc. Thousands of blogs, millions of tweets, and billions of emails are written each day. Among these social media platforms, Twitter is one of them that is gaining high popularity nowadays. It provides fast and efficient way to analyze customers’ perspectives toward a product or service. Developing a program for sentimental analysis is the method to be used to mark customer’s perceptions or his/her reviews toward a product. Twitter is a microblogging site that enables users to send updates in the form of reviews or messages to a group of followers. Based on the opinion reflected, a tweet can be classified as positive, negative, or neutral. In this paper, we are basically investigating the sentiment of Twitter messages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R. Mahalakshmi, S. Suseela, Big-SoSA: Social Sentiment Analysis and Data Visualization on Big Data
S.B. Mane, Y. Sawant, S. Kazi, V. Shinde, Real Time Sentiment Analysis of Twitter Data Using Hadoop
M.K. Danthala, Tweet analysis: twitter data processing using Apache Hadoop. Int. J. Core Eng. Manag. (IJCEM)
S. Judith Sherin Tilsha, M.S. Shobha, A survey on twitter data analysis techniques to extract public opinion. Int. J. Adv. Res. Comput. Sci. Softw. Eng.
S. Nadagoud, K.D. Naik, Market sentiment analysis for popularity of Flipkart. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET)
Online Resource of Hive Available on: http://hive.apache.org/
Online Resource of Flume Available on: https://flume.apache.org/
A. Go, R. Bhayani, L. Huang. Twitter sentiment classification using distant supervision. CS224N Project Report, pp. 1–12 (Stanford, 2009)
J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters, Commun. ACM 51(1), 107–113 (2008)
K. Shvachko, H. Kuang, S. Radia, R. Chansler, The Hadoop
S.A. Bahrainian, A. Dengel, Sentiment analysis using sentiment features, in Proceedings of WPRSM Workshop and the Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, Atlanta, USA (2013)
Online resource related to big data Hadoop availabe on: https://data-flair.training/blogs/hadoop-tutorial/
K. Bodnar, The Ultimate List: 300 + Social Media Statistics. http://blog.hubspot.com/blog/tabid/6307/bid/5965/The-Ultimate-List-300-Social-Media-Statistics.aspx?source=Webbiquity (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mishra, R.K., Lata, S., Kumari, S. (2020). Twitter Sentimental Analytics Using Hive and Flume. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-0633-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0632-1
Online ISBN: 978-981-15-0633-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)