International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Analysis Of Behavior Extraction On Social Life Issues Using Tweets By Deep Learning Technique

[Full Text]



Obaidullah, Faiyaz Ahmad



Sentiment Analysis, Opinion Mining, Machine Learning, automated classification, social networks.



Sentiment analysis is also recognized as opinion mining. It exploits natural language processing (NLP), text analysis and computational linguistics to discover and dig up prejudiced information from the source materials. Sentiment analysis intends to establish the approach of a critic or an orator with respect to an exact topic or the overall contextual polarity of a manuscript. In this paper we aim to propose a deep learning approach to perform sentiment analysis of social media user reviews We exploit the conception of natural language processing(NLP) to find out meaningful tweets and then use Naïve Bayes method to classify all tweets.



[1]. Nakov, Preslav, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. "SemEval-2016 task 4: Sentiment analysis on Twitter." In Proceedings of the 10th international workshop on semantic evaluation (semeval-2016), pp. 1-18. 2016.
[2]. Severyn, Aliaksei, and Alessandro Moschitti. "Twitter sentiment analysis with deep convolutional neural networks." In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959-962. ACM, 2015.
[3]. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521, no. 7553 pp. 436, 2015.
[4]. Alvi, Muhammad Bux, Naeem A. Mahoto, Majdah Alvi, Mukhtiar A. Unar, and M. Akram Shaikh. "Hybrid Classification Model for Twitter Data-A Recursive Preprocessing Approach." In 2018 5th International Multi-Topic ICT Conference (IMTIC), pp. 1-6. IEEE, 2018.
[5]. Trupthi, M., Suresh Pabboju, and G. Narasimha. "Sentiment analysis on twitter using streaming API." In 2017 IEEE 7th International Advance Computing Conference (IACC), pp. 915-919. IEEE, 2017.
[6]. Siddiqua, Umme Aymun, Tanveer Ahsan, and Abu Nowshed Chy. "Combining a rule-based classifier with weakly supervised learning for Twitter sentiment analysis." 2016 International Conference on Innovations in Science, Engineering, and Technology (ICISET). IEEE, 2016.
[7]. Minab, Shokoufeh Salem, Mehrdad Jalali, and Mohammad Hossein Moattar. "A new sentiment classification method based on the hybrid classification on Twitter." In 2015 International Congress on Technology, Communication and Knowledge (ICTCK), pp. 295-298. IEEE, 2015.
[8]. Kirilenko, Andrei P., Svetlana O. Stepchenkova, Hany Kim, and Xiang Li. "Automated sentiment analysis in tourism: Comparison of approaches." Journal of Travel Research 57, no. 8, pp. 1012-1025, 2018.
[9]. Shamal, Achira Jeewaka, Rankothge Gishan Hiranya Pemathilake, Sachith Paramie Karunathilake, and Gamage Upeksha Ganegoda. "Sentiment Analysis using Token2Vec and LSTMs: User Review Analyzing Module." In 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 48-53. IEEE, 2018.
[10]. Faiyaz Ahmad, Manuj Darbari Rishi Asthana, “ Different Approaches of Soft Computing Techniques (Inference System) which are used in Clinical Decision Support System for Risk-based Prioritization”, Asian Journal of Computer and Information Systems (ISSN: 2321 – 5658) Volume 03 – Issue 01, 2015.