Sentiment Analysis of Social Networking Data Using Categorized Dictionary

Document Type: Research Paper


1 Associate Prof., Department of CSE, ASET, Amity University Uttar Pradesh, Noida.

2 The North Cap University, Gurgaon, India.

3 Associate Prof., Department of ECE, KIET Group of Institutions, Ghaziabad, India.

4 Assistant Prof., The North Cap University, Gurgaon, India.



Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed.  A categorized dictionary is developed for the sentiment classification and further calculation of sentiment accuracy. The concept of categorized dictionary involves the creation of dictionaries for different categories making the comparisons specific. The categorized dictionary includes words defining the positive and negative sentiments related to the particular category. It is used by the mapper reducer algorithm for the classification of sentiments. The data is collected from social networking site and is pre-processed. Since the amount of data is enormous therefore a reliable open-source framework Hadoop is used for the implementation. Hadoop hosts various software utilities to inspect and process any type of big data. The comparative analysis presented in this paper proves the worthiness of the proposed method.


Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68.
Chawda, R. K., & Thakur, G. (2016, March). Big data and advanced analytics tools. In 2016 symposium on colossal data analysis and networking (CDAN) (pp. 1-8). IEEE.
Dasgupta, S. S., Natarajan, S., Kaipa, K. K., Bhattacherjee, S. K., & Viswanathan, A. (2015, October). Sentiment analysis of Facebook data using Hadoop based open source technologies. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-3). IEEE.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
Goswami, S., Nandi, S., & Chatterjee, S. (2019). Sentiment analysis based potential customer base identification in social media. In Contemporary Advances in Innovative and Applicable Information Technology (pp. 237-243). Springer, Singapore.
Gupta, P., Kumar, P., & Gopal, G. (2015). Sentiment analysis on Hadoop with Hadoop streaming. International Journal of Computer Applications, 121(11).
Gupta, P., Sharma, A., & Grover, J. (2016, September). Rating based mechanism to contrast abnormal posts on movies reviews using MapReduce paradigm. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 262-266). IEEE.
Kang, G. J., Ewing-Nelson, S. R., Mackey, L., Schlitt, J. T., Marathe, A., Abbas, K. M., & Swarup, S. (2017). Semantic network analysis of vaccine sentiment in online social media. Vaccine, 35(29), 3621-3638.
Kumar, B. (2015). An encyclopedic overview of ‘big data’analytics. International Journal of Applied Engineering Research, 10(3), 5681-5705.
Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior, 31, 527-541.
Patidar, K., & Sharma, I. (2015). Study of Big Data Analysis Tools and Techniques.
Selvan, L. G. S., & Moh, T. S. (2015, June). A framework for fast-feedback opinion mining on Twitter data streams. In 2015 International Conference on Collaboration Technologies and Systems (CTS) (pp. 314-318). IEEE.
Tayal, D. K., & Yadav, S. K. (2016, March). Fast retrieval approach of sentimental analysis with implementation of bloom filter on Hadoop. In 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) (pp. 14-18). IEEE.