Open Access   Article

Implementation of a Generalized, Real Time and Natural Language Processing Based Opinion Mining System for Twitter

Urmita Sharma1 , Dhanraj Verma2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 59-66, Jan-2019


Online published on Jan 31, 2019

Copyright © Urmita Sharma, Dhanraj Verma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library


IEEE Style Citation: Urmita Sharma, Dhanraj Verma, “Implementation of a Generalized, Real Time and Natural Language Processing Based Opinion Mining System for Twitter”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.59-66, 2019.

MLA Style Citation: Urmita Sharma, Dhanraj Verma "Implementation of a Generalized, Real Time and Natural Language Processing Based Opinion Mining System for Twitter." International Journal of Computer Sciences and Engineering 7.1 (2019): 59-66.

APA Style Citation: Urmita Sharma, Dhanraj Verma, (2019). Implementation of a Generalized, Real Time and Natural Language Processing Based Opinion Mining System for Twitter. International Journal of Computer Sciences and Engineering, 7(1), 59-66.

113 83 downloads 17 downloads


Success of any company or product depends on customer’s satisfaction. If customers do not satisfied with the services or product provided by company, then certainly company needs to improve it. Opinion mining (OM) can help in doing this. OM is the process of computationally identifying and categorizing opinions from piece of text and determines whether the writer’s attitude towards a particular topic or the product is positive, negative or neutral. This paper proposed a training model using sentdex data set to train the OM algorithm. This algorithm is based on supervised machine learning model to calculate OM of given text. Entire system is developed to calculate opinion from tweeters feeds. This system is working on real time data. Proposed system is designed for open field. One can take opinion of many field like political issue, product, company, person etc. this paper also presented the comparison of proposed results with well known python textblob API. textblob is used to perform many texts based operations. Sentiment analysis (OM) is one of them. In many OM systems this API is used.

Key-Words / Index Term

Opinion Mining, Machine Learning, NLP, textblob, sentdex, NLTK


[1] Farhan Hassan Khan, Saba Bashir and Usman Qamar, “TOM: Twitter opinion mining framework using hybrid classification scheme”, Decision Support Systems, Vol. 57, pp. 245–257 , 2014.
[2] Marıa del Pilar Salas, Rafael Valencia, Antonio Ruiz and Ricardo Colomo, “Feature-based opinion mining in financial news: An ontology-driven approach”, Journal of Information Science, Vol. 34, Issue 4, pp. 458-479, 2016.
[3] Nuno Oliveira, Paulo Cortez and Nelson Areal, “The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices”, Expert Systems With Applications, Vol. 73, pp. 125–144, 2017.
[4] Shiliang Sun, Chen Luo, Junyu Chen, “A Review of Natural Language Processing Techniques for Opinion Mining Systems”, Information Fusion, Vol. 36, pp. 10-25, 2017.
[5] R. Piryani, D. Madhavi and V.K. Singh, “Analytical mapping of opinion mining and sentiment analysis research during 2000–2015”, Information Processing and Management, Vol. 53, pp. 122-150, 2017.
[6] Mangi Kang, Jaelim Ahn and Kichun Lee, “Opinion mining using ensemble text hidden Markov models for text classiÞcation”, Expert Systems With Applications ,Vol. 94, pp. 218-227, 2018.
[7] M. Rathan, Vishwanath R. Hulipalled, K.R. Venugopal and L.M. Patnaik, “Consumer Insight Mining: Aspect Based Twitter Opinion Mining of Mobile Phone Reviews”, Applied Soft Computing Journal, Vol. 68, pp. 765-773, 2018.
[8] Betoul Duondar,Diyar Akay, Fatih Emre Boran and Suat Ozdemir, “Fuzzy Quantification and Opinion Mining on Qualitative Data using Feature Reduction”, International Journal of Intelligent System, Vol. 33, Issue 9, pp. 1840–1857, 2017.
[9] Soujanya Poria, Erik Cambria and Alexander Gelbukh, “Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network”, Knowledge-Based Systems, Vol. 108, pp. 42-49, 2016.
[10] Bird, Steven, Edward Loper and Ewan Klein, “Natural Language Processing with Python”, O’Reilly Media Inc., 2009.
[11] Shrija Madhu, “An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.34-36, 2018.
[12] Ketan Sarvakar, Urvashi K Kuchara, “Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, Vol.6, Issue.3, pp. 8-12 , 2018