Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

A Novel Approach for Extraction of Distinguishing Emotions for Semantic Granularity Level Sentiment Analysis in Multilingual Context

Author(s): Midde V. Naik*, Vasumathi D and A.P. Siva Kumar

Volume 15, Issue 1, 2022

Published on: 18 September, 2020

Page: [77 - 87] Pages: 11

DOI: 10.2174/2666255813999200918123059

Price: $65

Abstract

Introduction: Extraction of distinguishing semantic level emotions posed in multilanguages over social media is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian or local languages combining with multilanguages over social media has become an essential challenge in the field of big data sentiment analysis.

Methods: In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with the help of lexicon-based features groups.

Results: The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multi-languages text.

Conclusion: Machine learning technique SVM has outperformed for sentiment and emotion extraction.

Keywords: Emotion extraction, sentiment analysis, multi-languages, machine learning, dravidian languages, semantic level analysis.

Graphical Abstract

[1]
I. Sabatovych, "Use of sentiment analysis for predicting public opinion on referendum: A feasibility study", Ref. Libr., vol. 60, no. 3, pp. 202-211, 2019.
[http://dx.doi.org/10.1080/02763877.2019.1595260]
[2]
A. Abbasi, H. Chen, and A. Salem, "Sentiment analysis in multiple languages: Features selection for opinion classification in web forums", ACM Trans. Inf. Syst., vol. 26, no. 3, p. 12, 2008.
[http://dx.doi.org/10.1145/1361684.1361685]
[3]
Y. Zhang, W. Ren, and T. Zhu, "MoSa: A modeling and sentiment analysis system for mobile application big data", J. Symmetry, vol. 11, no. 1, p. 115, 2019.
[4]
V.K. Jain, and S. Kumar, "An effective approach to track levels of influenza-A (H1N1) pandemic in India using Twitter", Procedia Comput. Sci., vol. 70, no. 1, pp. 801-807, 2015.
[http://dx.doi.org/10.1016/j.procs.2015.10.120]
[5]
P. Ekman, "An argument for basic emotions", Cogn. Emotion, vol. 6, pp. 169-200, 1992.
[http://dx.doi.org/10.1080/02699939208411068]
[6]
C.E. Izard, Human Emotions., Plenum Press: Newyork, 1977, pp. 28-46.
[http://dx.doi.org/10.1007/978-1-4899-2209-0]
[7]
D. Ghazi, D. Inkpen, and S. Szpakowicz, "Hierarchical versus flat classification of emotions in the text", In Proceedings of the NAACL, HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotions in Text, 2010pp. 140-146
[8]
D. Milne, C. Paris, H. Christensen, P. Batterham, and B. O’Dea, "We feel: Talking the emotional pulse of the world", In Proceedings 19th Triennial Congress of the International Ergonomics Association, vol. 9, p. 14, 2015.
[9]
S.L. Fernandes, and G.J. Bala, "Fusion of sparse representation and dictionary matching for identification of humans in uncontrolled environment", Comput. Biol. Med., vol. 76, pp. 215-237, 2016.
[http://dx.doi.org/10.1016/j.compbiomed.2016.07.007 ] [PMID: 27498411]
[10]
S. Aman, and S. Szpakowicz, "Using Roget’s thesaurus for fine-grained emotion recognition", In Proceedings in 3rd International Joint Conference on Natural Language Processing, 2008pp. 296-302
[11]
B. Ofoghi, M. Mann, and K. Verspoor, "Towards the early discovery of silent health threats: A social media emotion classification technique", In 2016 Proceedings in a Pacific Symposium on Biocomputing, 2016pp. 504-515
[12]
S.S. Mukku, S.R. Oota, and R. Mamidi, "Tag me a label with multi-arm: Active learning for Telugu sentiment analysis", In International Conference on Big Data Analytics and Knowledge Discovery, 2017pp. 355-367
[http://dx.doi.org/10.1007/978-3-319-64283-3_26]
[13]
N. Li, and D.D. Wu, "using text mining and sentiment analysis for online forums hotspot detection and forecast", Decis. Support Syst., vol. 48, no. 2, pp. 354-368, 2010.
[http://dx.doi.org/10.1016/j.dss.2009.09.003]
[14]
B. Ofoghi, M. Mann, and K. Verspoor, "Towards the early discovery of salient health threats: A social media emotion classification technique", Pac. Symp. Biocomput., vol. 21, pp. 504-515, 2016.
[http://dx.doi.org/10.1142/9789814749411_0046] [PMID: 26776213]
[15]
"Marketing Analytics & Reporting Software", Postano.com.. Available at: http://www.postano.com/blog/how-social-media-is-changingsports-marketing [Accessed: 23-Nov-2021].
[16]
X. Wang, "Applying the integrative model of behavioral prediction and attitude functions in the context of social media use while viewing mediated sports", Comput. Human Behav., vol. 29, pp. 1538-1545, 2013.
[17]
Y. Yu, and X. Wang, "World cup 2014 in the Twitter world: A big data analysis of sentiments in US sports fans tweets", Comput. Human Behav., vol. 48, pp. 392-400, 2015.
[18]
S.S. Mukku, N. Choudhary, and R. Mamidi, "Enhanced sentiment classification of Telugu text using ML Techniques", In SAAIP International Joint Conference on Artificial Intelligencne, 2016pp. 29-34
[19]
P. Arora, A. Bakliwal, Madhappan S., Kapre N., Singh M., and V. Varma, "Mining sentiments from tweets", In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, 2012pp. 11-18
[20]
M.V. Naik, D. Vasumathi, and A.S. Kumar, "An enhanced unsupervised learning approach for sentiment analysis using extraction of tri-co-occurrence words phrases", In Proceedings of the Second International Conference on Computational Intelligence and Informatics, 2018pp. 17-26
[21]
R. Mohanty, V. Naik, and A. Mubeen, "Software reliability prediction using ant colony optimization technique", In 2014 Fourth International Conference on Communication Systems and Network Technologies (ICCSN), 2014pp. 496-500
[22]
M.V. Naik, and R. Mohanty, "An expert system approach for legal reasoning in acquiring immovable property", In 2014 First International Conference on Networks and Soft Computing (ICNSC), 2014pp. 370-374
[23]
M.V. Naik, and S.S. Reddy, "An innovative optimized model to anticipate clients about immigration in the telecom industry", IEEE Conference (ICATCCT), 2017pp. 232-236
[24]
M. Narsimlu, M.V. Naik, and M.R. Sree, "An innovative research on augmented reality to control the status of physical light using Maya as part of IoT", In 2017 International Conference On Smart Technologies For Smart Nation, 2017pp. 1221-1227
[25]
M.V. Naik, and S. Lokhanday, "Building a legal expert system for legal reasoning in specific domain- A survey", Int. J. Comput. Sci. Inf. Technol., vol. 4, no. 5, pp. 175-184, 2016.
[26]
V. Guda, I. Srujana, and M.V. Naik, "Reasoning in legal text documents with extracted event information", Int. J. Comput. Appl., vol. 28, no. 7, pp. 8-13, 2011.
[27]
V.N. Rao, and M.V. Naik, "An enhanced model to extract expected answer for temporal questions over question answering system", International Journal of Innovative Research in Technology & Science (IJIRTS), vol. 2, no. 5, pp. 27-34, 2013.
[28]
K. J. Rani, and M. V. Naik, "Machine learning intelligent techniques and bio-inspired computing techniques applications for software engineering problems - A survey", Republication, vol. 10, no. 81, pp. 130-152.
[29]
V.V. Raju, K. Gurugubelli, and A.K. Vuppala, "Application of emotion recognition and modification for emotional telugu speech recognition", Mob. Netw. Appl., vol. 24, no. 1, pp. 193-201, 2019.
[http://dx.doi.org/10.1007/s11036-018-1052-9]
[30]
K. Mannepalli, S. Maloji, and P. Narahari, "Text independent emotion recognition for Telugu speech by using prosodic features", Int. J. Eng. Technol., vol. 7, no. 27, pp. 594-596, 2018.
[http://dx.doi.org/10.14419/ijet.v7i2.7.10887]
[31]
B.S. Yalamanchili, K.K. Anusha, K. Santhi, P. Sruthi, and B. Swapnamadhavi, "Non-linear classification for emotion detection on telugu corpus", Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 2, pp. 2443-2448, 2014.
[32]
S. Manchala, D.C. Mohan, and A. Nagesh, "Word and sentence level emotion analyzation in Telugu blog and news", Int. J. Comput. Sci. Eng. Appl., vol. 2, no. 3, pp. 183-197, 2012.
[http://dx.doi.org/10.5121/ijcsea.2012.2317]
[33]
P. Arora, A. Bakliwal, V. Varma, and H.S. Lexicon, "A lexical resource for Hindi polarity classification", In Proceedings of the 8th International Conference on Language Resources and Evaluation (ILREC), 2012pp. 1189-1196
[34]
P. Arora, A. Bakliwal, A. Patil, and V. Varma, "Towards enhanced opinion classification using NLP techniques", In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology, 2011pp. 101-107
[35]
P. Jonnalagadda, K.P. Hari, S. Batha, and H. Boyina, “A rule-based sentiment analysis in Telugu”, International Journal of Advance Research Ideas and Innovations in Technology., IJARIIT, 2019, pp. 387-390.
[36]
R.R. Gangula, and R. Mamidi, "Resource creation towards automated sentiment analysis in Telugu (a low resource language) and integrating multiple domain sources to enhance sentiment prediction", In Proceedings of the Eleventh International Conference on Language Resources and Evaluation, 2018.https://www.aclweb.org/anthology/L18-1100.pdf
[37]
P. Sharma, and T.S. Moh, "Prediction of Indian election using sentiment analysis on Hindi Twitter", IEEE International Conference on Big Data, 2016pp. 1966-1971
[http://dx.doi.org/10.1109/BigData.2016.7840818]
[38]
S. Aman, and S. Szpakowicz, "Identifying expressions of emotion in text", Lecture Notes in Computer Science Springer, vol. 4629, pp. 196-205, 2007.
[39]
Available at: http://sentiwordnet.isti.cnr.it/ [Accessed: 23-Nov-2021]

Rights & Permissions Print Cite
Article Metrics
11
1
© 2024 Bentham Science Publishers | Privacy Policy