Reviewing Sentiment Analysis at the Shallow End

  • Francisca Oladipo Federal University Lokoja
  • Ogunsanya, F. B Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Musa, A. E. Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Ogbuju, E. E Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
  • Ariwa, E. Federal University Lokoja, Nigeria, 5University of Herdforshire, UK
Keywords: sentiment analysis, classification, supervised machine learning, unsupervised machine learning


The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.



(1) Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums,. In ACM Transactions on Information Systems, 26(3), 1-34.

(2) Agarwal, S., & Sureka, A. (2014). A focused crawler for mining hate and extremism promoting videos on youtube. Proceedings of the 25th ACM conference on Hypertext and Social media, 294–296.

(3) Ahmed, S., & Qadoos, M. (2018, August 20-30). Terrorism detection by Tweet sentimental analysis. MDSRIC-2018 Proceedings, 1-5.

(4) Anuja, P. J., & Padma, D. (2016). Application of machine learning techniques to sentiment analysis. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 628-632.

(5) Apoorv, A., Boyi, X., Ilia, V., Owen, R., & Rebecca, P. (2011, June). Sentiment analysis of Twitter data. Proceedings of the Workshop on Language in Social Media (LSM), 30-38.

(6) Ashcroft, M., Fisher, A., Lisa, K., Enghin, O., & Nico, P. (2015). Detecting Jihadist messages on Twitter. Intelligence and Security Informatics Conference(EISIC), European, 161-164.

(7) Bac, L., & Huy, N. (2015). Twitter sentiment analysis using machine learning techniques. Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, 358.

(8) Barbosa, L., & Feng, J. ( 2010, August). Robust sentiment detection on Twitter from biased and noisy data. In Proceedings of the International Conference on Computational Linguistics, 36-44.

(9) Bartlett, A. F. (2015). How to beat the media mujahideen. Retrieved from

(10) Blinov, P., Klekovkina, M., Kotelnikov, E., & Pestov, O. (2013). Research of lexical approach and machine learning methods for sentiment analysis. Computer Linguistics Intellectual Technology, 2, 48-58.

(11) CCINT. (2018). CyberCrime intelligence framework for detecting online radical content.

(12) Cunningham, Daniel, Everton, S. F., & Montery, R. S. (2014). The ISIL Twitter narrative. CA: Defense Analysis Department, Naval Postgraduate School.

(13) Dey, L., Biswas, S. &., Bose, A. &., Tiwari, B. &., & Sweta. (2016). Sentiment analysis of review datasets using Naive Bayes and K-NN classifier. International Journal of Information Engineering and Electronic Business, 54-62.

(14) Dos Santos, C., & Gatti de Bayser, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. International Conference on Computational Linguistics, 69-78.

(15) Ellman, J., & Chalothorn, T. (2012). Using SentiWordNet and sentiment analysis for detecting radical content on web forums.

(16) FBI. (2015). Retrieved March 10, 2019, from

(17) Ferrara, E., Waang, W., Varol, O., Flammini, A., & Galstyan, A. (2016). Predicting online extremism, content adopters, and interaction reciprocity. International Conference on Social Informatics, 22-39.

(18) Fisher, A. (2015). Last gang in town: How Jihadist network maintains a persistent presence online. In Perspectives of Terrorism.

(19) Fisher, A. N. (2004, August). The call up: The roots of a resilient and persistent jihadist presence on Twitter.

(20) Harb, A., Planti, M., Dray, G., Roche, M., Trousset, O., & Poncelet, P. (2008). Web opinion mining: how to extract opinions from blogs?. Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology. Cergy-Pontoise, France.

(21) Hartung, M., Klinger, R., Schmidtke, F., & Vogel, L. (2017). Identifying right-wing extremism in german Twitter profiles: A classification approach. International Conference on Applications of Natural Language to Information Systems, 320-325.

(22) Horsely, R. (1979). The Sicari: Ancient Jewish terrorists. The Journal of Religion, 435-458.

(23) Human, P., & Shikha, P. (2016). Sentiment analysis on the Twitter dataset using the Naive Bayes algorithm. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 416-419.

(24) Internet Users. (2014). Retrieved from

(25) ISIS. (2019, June 11). ISIS hashtag campaign. Retrieved from

(26) Kathy, L., Diana, P., Ramanathan, N., Md. Mostofa, A., Ankit, A., & Alok, C. (2011). Twitter trending topic classification. Proceedings of the IEEE 11th International Conference on Data Mining Workshops, 251-258.

(27) Khan, A., Baharudin, B., & Khan, K. (2011). Sentiment classification from online customer reviews using lexical contextual sentence structure. ICSECS 2011: 2nd International Conference on Web Intelligence and Intelligent Agent Technology, 317-331.

(28) Kim, S.-M., & Hovy, E. (2004). Determining the sentiment of opinions. Proceedings of the 20th International Conference on Computational Linguistics, 1367.

(29) Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter Sentiment Analysis: The Good the Bad and the OMG! Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 11, 538-541.

(30) Leena, A. D., & Narasingarao. (2019). Addressing social popularity in Twitter data using drift detection techniques. Journal of Engineering Science and Technology, 14, 922-934.

(31) Lui, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural language processing.

(32) Luiz, F. C., Nadia, F. d., Eduardo, R. H., & Estevam, R. H. (2014). Classification and CLustering for Tweet Sentiment Analysis. Brazillian Conference on Intelligent Systems, 210-215.

(33) Mei, J., & Frank, R. (2015). Sentiment Crawling: Extremist content collection through a sentiment analysis guided web-crawler. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining.

(34) Mejova, Y. (2019). Sentiment Analysis: An overview.

(35) Merari, A., & Friedland, N. (2007). Social psychological aspects of political terrorism in visualization. IEEE Symposium on Visual Analytics Science and Technology.

(36) Mittal, A., & Goel, A. (2011). Stock Prediction using Twitter data. Stanford CS229, 1-5.

(37) Mudinas, A., Zhang, D., & Levene, M. (2012). Combining lexicon and learning-based approaches for concept-level sentiment analysis. Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, 1-8.

(38) Nafees, A. F., Ritika, & Aayush, S. (2019, February). Sentiment Analysis of Twitter Accounts using Natural

Language Processing. International Journal of Engineering and Advanced Technology (IJEAT), 8, 473-479.

(39) Neethu, M. S., & Rajasree, R. (2013). Sentiment analysis on Twitter using machine learning techniques. Fourth International Conference on Computing, Communications, and Networking Technologies (ICCCNT), 1-5.

(40) Neri, F., Aliprandi, C., Capeci, F., Cuadros, M., & By, T. (2012). Sentiment Analysis on Social Media. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 919-926.

(41) Osimo, D., & Mureddu, F. (2010). Research challenge on opinion mining and sentiment analysis. The CROSSROAD Roadmap on ICT for Governance and Policy Modeling.

(42) Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of LREC.

(43) Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information extraction, 2, 1-135.

(44) Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: Sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, 10, 79-86.

(45) Paridhi, P., & Dinesh, D. P. (2018). Twitter sentiment classification using supervised lazy learning methods. International Journal of Innovative Research in Computer and Communication Engineering, 6.

(46) Paulo, A., Donald, C., & Ivens, P. (2015). The use of Machine Learning Algorithms in Recommender Systems: A systematic review. Expert Systems With Applications, 97.

(47) Pooja, W., & Bhatia, M. (2016). Classification of Radical Message on Twitter using the Security Association. In B. Issac, & N. Israr, Case Studies in secure computing: Achievements and Trends. (p. 273). Auerbach.

(48) Prerna, C., Soujanya, P., & Erik, C. ( 2015, June). SeNTU: Sentiment Analysis of Tweets by combining a Rule-based with supervised learning. . In Proceedings of the International Workshop on Semantic Evaluation (SemEval), Denver, CO, , 647-651.

(49) Ryan, S., & Richard, F. (2016). Sentiment-based classification of radical text on the web. European Intelligence and Security Informatics Conference., 104-107.

(50) Sayali, P. N., Prasad, S. N., Akshay, S. P., & Dr. Ingle, R. (2018). Sentiment analysis on Twitter. International Research Journal of Engineering and Technology (IRJET), 5.

(51) Shakeel, A., Muhammed, Z., Fahad, A., & A, I. (2019). Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Human-Centric computing and Information Sciences, 9-24. DOI:10.1186/s13673-019-0185-6

(52) Sloan, S., & Anderson, S. (2009). Historical dictionary of terrorism. Scarecrow Press.

(53) Sofea, A. A., & Izzatdin, A. A. (2017). Terrorism detection based on sentiment analysis using Machine Learning. Journal of Engineering and Applied Sciences,, 2, 691-698. DOI: 10.3923/jeasci.2017.691.698

(54) Suman, D., & Wenjun, Z. (2015). Social Multimedia Signals: A Signal Processing Approach to Social Network Phenomena.

(55) Thakkar, H., & Patel, D. (2015). Approaches for sentiment analysis on Twitter: A state-of-art study.

Department of Computer Engineering, Montreal, Quebec.

(56) Vishal, & Sonawane. (2016). Sentiment analysis of Twitter data: a survey of techniques. international journal of computer application, 139.

(57) Walid, M., Kareem, D., & Ingmar, W. (2015, March Monday,21.). #FailedRevolutions: Using Twitter to study the antecedents of ISIS support. DOI:10.5210/fm.v21i2.6372

(58) Walid, W. I. (2015). #failedrevolutions:. Using Twitter to study the antecedents of isis support., arXiv preprint arXiv:1503.02401.

(59) Wei, Y., Singh, L., & Marti, S. (2016). identification of extremists on Twitter. Proceedings of the IEE/ACM international conference on advances in social networks analysis and mining., 1251-1255.

(60) Yang, M., Kiang, M., Ku, Y. C., & Li, Y. (2011). Social Media analytics for radical opinion mining in hate group web forums. Journal of homeland security and Emergence management, 8.

(61) Yardon, D. (2016). The Guardian.

(62) Younas, M. (2014). Digital jihad and its significance to counterterrorism. Counter-Terrorism Trends, Anal, 10-17.

(63) Zeng, D., Wei, D., Chau, M., & Wang, F. (2011). Domain-specific Chinese word segmentation using suffix tree and mutual information. Information System Fontain. Retrieved from

How to Cite
Oladipo, F., F. B, O., A. E., M., E. E, O., & E., A. (2020). Reviewing Sentiment Analysis at the Shallow End . Transactions on Machine Learning and Artificial Intelligence, 8(4), 47-62.