Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Sentiment Analysis of Social Media Micro Blogs using Power Links and Genetic Algorithms

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Mahmoud Rokaya

Mahmoud Rokaya. Sentiment Analysis of Social Media Micro Blogs using Power Links and Genetic Algorithms. International Journal of Computer Applications 177(44):28-35, March 2020. BibTeX

	author = {Mahmoud Rokaya},
	title = {Sentiment Analysis of Social Media Micro Blogs using Power Links and Genetic Algorithms},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2020},
	volume = {177},
	number = {44},
	month = {Mar},
	year = {2020},
	issn = {0975-8887},
	pages = {28-35},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2020919967},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In the current years, social media became one of the most important sources of data for different data analytic purposes. One of the most important issues is how to map different trends in social media and define the relation between different groups based on their sentiment or interests. In this paper, a two-phase approach is used for clustering a set of blogs. At the first phase, the approach builds a lexicon that provides the polarity of each word. In the second phase, the approach clusters the blogs bases on the polarity features and the Power Link features. The output of the second phase is used as the input of the first phase to get an improved lexicon. This process will continue in a loop between phase one and phase 2 till a stable set of clusters is gotten. The approach aims to develop a non-supervised cluster agent that can correctly cluster micro blogs and define different interests of different groups of people. The results of the approach are expressed in terms of precision, recall and F-measure.


  1. Sarangi, S. K., Jaglan, V. and Dash, Y., 2013. A Review of Clustering and Classification Techniques in Data Mining, International Journal of Engineering, Business and Enterprise Applications (IJEBEA), 4(2), March-May, , pp. 140-145
  2. Aucouturier, J.-J. and Pachet, F., 2004, "Improving Timbre Similarity: How high's the sky?," in Journal of Negative Research Results in Speech and Audio Sciences.
  3. Kumar, V., and Rathee, N., 2011, ITM University, “Knowledge discovery from database Using an integration of clustering and classification”, International Journal of Advanced Computer Science and Applications, (March 2011) Vol. 2, No.3.
  4. Sharma, N., Bajpai, A. and Litoriya R., 2012, “Comparison the various clustering algorithms of weka tools”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, (May 2012), Volume 2, Issue 5.
  5. Devijver, P.A. and Kittler, J., 1982, Pattern Recognition: A Statistical Approach. PrenticeHall.
  6. H. Liu and H. Motoda, 1998, Feature Extraction, Construction and Selection: A Data Mining Perspective. Kluwer Academic.
  7. Rokaya, M., Ghiduk A. S., 2019, Arabic Lexicon Learning to Analyze Sentiment in Microblogs, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 8, 592-599
  8. Keshavarz, H. and Abadeh, M. S., 2017, ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs, Knowledge-Based Systems 122 , 1–16
  9. Haj mohammadi, M.S., Ibrahim and Selamat R. A., 2014, Cross-lingual sentiment classifica-tion using multiple source languages in multi-view semi-supervised learning, Eng. Appl. Artif. Intell. 36, 195–203 .
  10. Cui, Z., Shi, X. and Chen, Y., 2015, Sentiment analysis via integrating distributed representations of variable-length word sequence, Neurocomputing, 126–132 .
  11. Weissbock, J. and Inkpen, D., 2014, in: Combining Textual Pre-Game Reports and Statis- tical Data for Predicting Success in the National Hockey League, Advances in Artificial Intelligence, Springer International Publishing, pp. 251–262 .
  12. Javed, B.S., 2018, Hybrid semantic clustering of hashtags, Online Social Networks and Media 5 (2018) 23–36
  13. Vicient , A. M., 2014, Unsupervised semantic clustering of Twitter hashtags, Proceedings of the 21st European Conference on Artificial Intelligence, pp. 1119–1120 .
  14. Javed , B.S., 2016, Sense-level semantic clustering of hashtags in social me- dia, in: Proceedings of the 3rd Annual International Symposium on Informa- tion Management and Big Data, pp. 140–149 .
  15. Muntean, C.I., Morar , G.A., Moldovan, D., 2012, Exploring the meaning behind Twitter hashtags through clustering, Lect. Notes Bus. Inf. Process. 127, 231–242
  16. Bhulai, S., Kampstra, P., Kooiman, L., Koole, Deurloo, G., M. and CCing, B.K., 2012, Trend visualization on Twitter: what’s hot and what’s not?, Proceedings of the 1st International Conference on Data Analytics, Springer-Verlag, pp. 43–48
  17. Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley
  18. Pesina, S. and Yusupova, L. G., 2014, Words Functioning in Lexicon, 2nd Global Conference On Linguistics And Foreign Language Teaching, Linelt-, Dubai – United Arab Emirates, December 11 – 13, 2014
  19. Rokaya, M and Atlam, E. S., 2010, Building of field association terms based on links’, Int. J. Computer Applications in Technology, Vol. 38, No. 4, pp.298–305.
  20. Rokaya, M, 2013, Automatic Text Extraction Based on Field Association Terms and Power Links, International Journal of Computer and Information Technology (IJCIT), Volume 02– Issue 06 (November 2013), pp 1049- 1053
  21. Rokaya, M, 2013, Automatic Summarization based on Field Coherent Passages. International Journal of Computer Applications 79(9) (October 2013),38-44,. Published by Foundation of Computer Science, New York, USA
  22. Rokaya, M and Aljahdali, S., 2013, Building a Real Word Spell Checker Based on Power Links, International Journal of Computer Applications, Vol. 65 No 7,( March 2013), PP 14-19.
  23. Rokaya, M, Nahla, A. and Aljahdali, S., 2012, Context-Sensitive Spell Checking Based on Field Association Terms. IJCSNS International Journal Of Computer Science And Network Security. Vol. 12 No. 3 pp. 64-68.
  24. Rokaya, M, 2014, Improving Ranking of Search Engines Results Based on Power Links, IPASJ International Journal of Information Technology (IIJIT),Volume 2, Issue 9, September.


Sentiment Analysis, Evolution Calculation, Genetic Algorithms, Power Links, Social Media, Micro Blogs.