A Study of Sentiment and Trend Analysis Techniques for Social Media Content

Full Text (PDF, 254KB), PP.47-54

Views: 0 Downloads: 0


Asad Mehmood 1,* Abdul S. Palli 1 M. N. A. Khan 1

1. Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2014.12.07

Received: 11 Aug. 2014 / Revised: 14 Sep. 2014 / Accepted: 25 Oct. 2014 / Published: 8 Dec. 2014

Index Terms

Trend Analysis, Sentiment Analysis, Social Media Analysis, Semantic Web, Opinion Mining.


The social media networks have evolved rapidly and people frequently use these services to communicate with others and express themselves by sharing opinions, views, ideas etc. on different topics. The social media trend analysis is generally carried out by sifting the corresponding or interlinked events discussed on social media websites such as Twitter, Facebook etc. The fundamental objective behind such analyses is to determine the level of criticality with respect to criticism or appreciation described in the comments, tweets or blogs. The trend analysis techniques can also be systematically exploited for opinion making among the masses at large. The results of such analyses show how people think, assess, orate and opine about different issues. This paper primarily focuses on the trend detection and sentiment analysis techniques and their efficacy in the contextual information. We further discuss these techniques which are used to analyze the sentiments expressed within a particular sentence, paragraph or document etc. The analysis based on sentiments can pave way for automatic trend analysis, topic recognition and opinion mining etc. Furthermore, we can fairly estimate the degree of positivity and negativity of the opinions and sentiments based on the content obtained from a particular social media.

Cite This Paper

Asad Mehmood, Abdul S. Palli, M.N.A. Khan,"A Study of Sentiment and Trend Analysis Techniques for Social Media Content", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.12, pp.47-54, 2014. DOI:10.5815/ijmecs.2014.12.07


[1]Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World Wide Web (pp. 851-860). ACM.
[2]Pohl, D., Bouchachia, A., &Hellwagner, H. (2012). Automatic Identification of Crisis-Related Sub-Events using Clustering. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on (Vol. 2, pp. 333-338). IEEE.
[3]Kaplan, A. M., & Haenlein, M. (2011). The early bird catches the news: Nine things you should know about micro-blogging. Business Horizons, 54(2), 105-113.
[4]Lohmann, S., Burch, M., Schmauder, H., & Weiskopf, D. (2012, May). Visual analysis of microblog content using time-varying co-occurrence highlighting in tag clouds. In Proceedings of the International Working Conference on Advanced Visual Interfaces (pp. 753-756). ACM.
[5]Budak, C., Agrawal, D., & El Abbadi, A. (2011). Structural trend analysis for online social networks. Proceedings of the VLDB Endowment, 4(10), 646-656.
[6]Bloom, K., Garg, N., & Argamon, S. (2007). Extracting appraisal expressions.HLT-NAACL 2007, 308-315.
[7]Hasan, S. S., & Adjeroh, D. A. (2011). Proximity-based sentiment analysis. In Applications of Digital Information and Web Technologies (ICADIWT), 2011 Fourth International Conference on the (pp. 106-111). IEEE.
[8]Lin, Y. C., Yang, P. C., Hsieh, W. T., & Seng-cho, T. C. Technology Trend Analysis Tool using Twitter as a Source.
[9]Asur, S., &Huberman, B. A. (2010). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.
[10]Tumasjan, A., Sprenger, T. O., Sandner, P. G., &Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the fourth international AAAI conference on weblogs and social media (pp. 178-185).
[11]Wegrzyn-Wolska, K., & Bougueroua, L. (2012). Tweets mining for French Presidential Election. In Computational Aspects of Social Networks (CASoN), 2012 Fourth International Conference on (pp. 138-143). IEEE.
[12]Asur, S., Huberman, B. A., Szabo, G., & Wang, C. (2011). Trends in social media: Persistence and decay. In 5th International AAAI Conference on Weblogs and Social Media.
[13]Yu, L., Asur, S., &Huberman, B. A. (2011). What trends in chinese social media. arXiv preprint arXiv:1107.3522.
[14]Achrekar, H., Gandhe, A., Lazarus, R., Yu, S. H., & Liu, B. (2011). Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 702-707). IEEE.
[15]Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L., & Hsu, M. C. (2011, October). Visual sentiment analysis on twitter data streams. In Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on (pp. 277-278). IEEE.
[16]Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of LREC (Vol. 2010).
[17]Lima, A. C., & de Castro, L. N. (2012, November). Automatic sentiment analysis of Twitter messages. In Computational Aspects of Social Networks (CASoN), 2012 Fourth International Conference on (pp. 52-57). IEEE.
[18]Cvijikj, I. P., & Michahelles, F. (2011). Monitoring trends on facebook. In Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on (pp. 895-902). IEEE.
[19]Li, G., & Liu, F. (2010, November). A clustering-based approach on sentiment analysis. In Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on (pp. 331-337). IEEE.
[20]Pohl, D., Bouchachia, A., & Hellwagner, H. (2012). Automatic Identification of Crisis-Related Sub-Events using Clustering. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on (Vol. 2, pp. 333-338). IEEE.
[21]Corley, C. D., Mikler, A. R., Singh, K. P., & Cook, D. J. (2009). Monitoring influenza trends through mining social media. In International Conference on Bioinformatics & Computational Biology (pp. 340-346).
[22]Suzumura, T., & Oiki, T. (2011). StreamWeb: Real-Time Web Monitoring with Stream Computing. In Web Services (ICWS), 2011 IEEE International Conference on (pp. 620-627). IEEE.
[23]Karamibekr, M., & Ghorbani, A. A. (2012, December). Verb Oriented Sentiment Classification. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on (Vol. 1, pp. 327-331). IEEE.
[24]C. Fellbaum. Wordnet: An electronic lexical database.
[25]Cai, K., Spangler, S., Chen, Y., & Zhang, L. (2008, December). Leveraging sentiment analysis for topic detection. In Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT'08. IEEE/WIC/ACM International Conference on (Vol. 1, pp. 265-271). IEEE.
[26]Koncz, P., & Paralic, J. (2011, June). An approach to feature selection for sentiment analysis. In Intelligent Engineering Systems (INES), 2011 15th IEEE International Conference on (pp. 357-362). IEEE.
[27]Mizumoto, K., Yanagimoto, H., & Yoshioka, M. (2012, May). Sentiment Analysis of Stock Market News with Semi-supervised Learning. In Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on (pp. 325-328). IEEE.
[28]Colbaugh, R., & Glass, K. (2011, September). Agile Sentiment Analysis of Social Media Content for Security Informatics Applications. In Intelligence and Security Informatics Conference (EISIC), 2011 European (pp. 327-331). IEEE.
[29]Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003, May). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1 (pp. 173-180). Association for Computational Linguistics.
[30]Iqbal S., Khalid M., Khan, M N A. A Distinctive Suite of Performance Metrics for Software Design. International Journal of Software Engineering & Its Applications, 7(5), (2013).
[31]Iqbal S., Khan M.N.A., Yet another Set of Requirement Metrics for Software Projects. International Journal of Software Engineering & Its Applications, 6(1), (2012).
[32]Faizan M., Ulhaq S., Khan M N A., Defect Prevention and Process Improvement Methodology for Outsourced Software Projects. Middle-East Journal of Scientific Research, 19(5), 674-682, (2014).
[33]Khan K., Khan A., Aamir M., Khan M N A., Quality Assurance Assessment in Global Software Development. World Applied Sciences Journal, 24(11), (2013).
[34]Amir M., Khan K., Khan A., Khan M N A., An Appraisal of Agile Software Development Process. International Journal of Advanced Science & Technology, 58, (2013).
[35]Rehman T U., Khan M N A., Riaz N., Analysis of Requirement Engineering Processes, Tools/Techniques and Methodologies. International Journal of Information Technology & Computer Science, 5(3), (2013).
[36]Umar M., Khan, M N A., A Framework to Separate Non-Functional Requirements for System Maintainability. Kuwait Journal of Science & Engineering, 39(1 B), 211-231, (2012).
[37]Umar M., Khan, M. N. A, Analyzing Non-Functional Requirements (NFRs) for software development. In IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS), 2011 pp. 675-678), (2011).
[38]Khan, M. N. A., Chatwin, C. R., & Young, R. C. (2007). A framework for post-event timeline reconstruction using neural networks. digital investigation, 4(3), 146-157.
[39]Khan, M. N. A., Chatwin, C. R., & Young, R. C. (2007). Extracting Evidence from Filesystem Activity using Bayesian Networks. International journal of Forensic computer science, 1, 50-63.
[40]Khan, M. N. A. (2012). Performance analysis of Bayesian networks and neural networks in classification of file system activities. Computers & Security, 31(4), 391-401.
[41]Rafique, M., & Khan, M. N. A. (2013). Exploring Static and Live Digital Forensics: Methods, Practices and Tools. International Journal of Scientific & Engineering Research 4(10): 1048-1056.
[42]Bashir, M. S., & Khan, M. N. A. (2013). Triage in Live Digital Forensic Analysis. International journal of Forensic Computer Science 1, 35-44.
[43]Faizan M., Khan M NA., Ulhaq S., Contemporary Trends in Defect Prevention: A Survey Report. International Journal of Modern Education & Computer Science, 4(3), (2012).
[44]Khan, MNA., Khalid M., ulHaq S., Review of Requirements Management Issues in Software Development. International Journal of Modern Education & Computer Science, 5(1), (2013).