Abstract
People, these days, express their opinions regarding any particular topic or issue widely on social media. One such popular social media platform among masses is twitter with over 320 million monthly users. Users also express their thoughts on any political announcements or decisions taken by a particular party. Analyzing these tweets on a specific topic can help in determining what people think about measures undertaken by the government. It will give an idea on how many percent of people are in favor of any announcement, and how many of them stand against it. This will in turn provide areas of improvement for the ruling or opposition party. This paper thus aims on finding sentiments of tweets on a political leader, some party or announcements like a union budget. This can further be generalized to any particular measure undertaken by any organization.
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
Phand SA, Phand JA (2017) Twitter sentiment classification using stanford NLP. In: 2017 1st International conference on intelligent systems and information management (ICISIM)
Azam N, Jahiruddin, Abulaish M, SMIEEE, Haldar NAH (2015) Twitter data mining for events classification and analysis. In: Proceedings of the 2nd international conference on soft computing and machine intelligence (ISCMI’15). IEEE CPS, Hong Kong, Nov 23–24
Wang H, Can D, Kazemzadeh A, Bar F, Narayanan S (2012) A system for real-time twitter sentiment analysis of 2012 U S presidential election cycle. In: Proceedings of the 50th annual meeting of the association for computational linguistics. pp 115–120, Jeju, Republic of Korea, July 8–14
Guha S, Joshi A, Varma V (2015) Sentibase: sentiment analysis in twitter on a budget. In: SEM 4th joint conference on lexical and computational semantics denver, Colorado, USA. Report No: IIIT/TR/2015/-1
Norman J, Mangayarkarasi R, Vanitha M, Praveen Kumar T, UmaMaheswari G (2017) A Naive-Bayes strategy sentiment for sentiment analysis on demonetization and Indian budget 2017-case-study. Int J Pure Appl Math 117(17):23–31. ISSN: 1311-8080
Naiknaware B (2018) Peoples opinion on Indian budget using sentiment analysis techniques. Int J Res Eng Appl Manag (IJREAM). ISSN: 2454-9150, Special Issue-NCCT (2018)
Kaur J (2016) A review paper on twitter sentiment analysis techniques. Int J Res Appl Sci Eng Tech (IJRASET) 4(X). Guru Nanak Dev Engineering College, Ludhiana, Oct 2016, IC Value: 13.98, ISSN: 2321-9653
Sarlan A, Nadam C, Basri S (2014) Twitter sentiment analysis. In: 2014 International conference on information technology and multimedia (ICIMU). Putrajaya, Malaysia, Nov 18–20
Verma A, Singh KPA, Kanjilal K (2015) Knowledge discovery and twitter sentiment analysis: mining public opinion and studying its correlation with popularity of Indian movies. Int J Manag (IJM) 6(1):697–705. ISSN 0976–6502
Rahman E-U, Sarma R, Sinha R, Sinha P, Pradhan P (2018) A survey on twitter sentiment analysis. Int J Comput Sci Eng 6(11). Open Access Survey Paper, India, e-ISSN: 2347-2693
Kouloumpis E, Wilson T, Moore J, Twitter sentiment analysis: the good the bad and the OMG! In: Proceedings of fifth international AAAI conference on weblogs and social media (ICWSM)
Xu S, Li Y, Wang Z, Bayesian multinomial naive bayes classifier to text classification. In: International conference on multimedia and ubiquitous engineering international conference on future information technology
Heba M, Ismail, Harous S, Belkhouche B (2016) A comparative analysis of machine learning classifiers for twitter sentiment analysis. In: 17th International Conference on Intelligent Text Processing and Computational Linguistics-CICLing
Shinde PD, Rathod S (2018) A comparative study of sentiment analysis techniques. Int J Innov Adv Comput Sci 7(3). ISSN 2347–8616
Tugores A, Colet P (2013) Mining online social networks with Python to study urban mobility. In: Proceedings of the 6th European conference on python in science
Dhanush M, Ijaz Nizami S, Patra A, Biswas P, Immadi G (2018) Sentiment analysis of a topic on twitter using tweepy. Int Res J Eng Tech 5(5):2881. e-ISSN: 2395-0056
Jagannatha S, Niranjanamurthy M, Manushree SP, Chaitra GS (2014) Comparative study on automation testing using selenium testing framework and QTP. IJCSMC 3(10):258–267. ISSN 2320–088X
Anand N, Kumar T (2017) Text and emotion analysis of twitter data. Int J Comput Sci Eng 5(6). Open Access Research Paper, e-ISSN: 2347-2693
Chirawichitchai N (2013) Sentiment classification by a hybrid method of greedy search and multinomial naïve bayes algorithm. In: 2013 Eleventh international conference on ICT and knowledge engineering
Gupta B, Negi M, Vishwakarma K, Rawat G, Badhani P (2017) Study of twitter sentiment analysis using machine learning algorithms on python. Int J Comput Appl 165(9):(0975–8887)
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
Bhatia, G., Patil, C., Naik, P., Pingle, A. (2020). Tweet-Based Sentiment Analyzer. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-15-0630-7_36
Download citation
DOI: https://doi.org/10.1007/978-981-15-0630-7_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0629-1
Online ISBN: 978-981-15-0630-7
eBook Packages: EngineeringEngineering (R0)