Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 4/2020

03.02.2020

A comparative study on bio-inspired algorithms for sentiment analysis

verfasst von: Ashima Yadav, Dinesh Kumar Vishwakarma

Erschienen in: Cluster Computing | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data mining is one of the most explored and ongoing areas of research. Sentiment analysis is a popular application of data mining, where the information regarding the customer's emotions or attitude is extracted by applying various methods or techniques. The earlier work in sentiment analysis deals with supervised, unsupervised machine learning-based approaches and lexicon-based approaches. Nature-inspired algorithms are recently becoming an emerging topic of research for developing new algorithms and for optimizing the results as nature serves as an excellent source of inspiration. These techniques are divided into bio-inspired algorithms, physics–chemistry based algorithms, and others. This survey mainly deals with bio-inspired algorithms, which consist of swarm intelligence based and non-swarm intelligence-based algorithms. We present a comprehensive review of the significant bio-inspired algorithms that are popularly applied in sentiment analysis. We discuss state-of-the-art on these significant algorithms along with a comparative study on these algorithms by reviewing eighty articles from various journals, conferences, book chapters, etc. Finally, this review draws some essential conclusions and identifies some research gaps to motivate researchers in this area.
Vorheriger Artikel Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach
Nächster Artikel Satisfaction aware QoS-based bidirectional service mapping in cloud environment

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, no. 1, pp. 1–38 (2010)
2.
3.
Agarwal, A., Yadav, A., Vishwakarma, D.K.: Multimodal sentiment analysis via RNN variants. In: Proceedings—2019 IEEE/ACIS 4th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2019, pp. 19–23 (2019)
4.
Yadav, A., Agarwal, A., Vishwakarma, D.K.: XRA-net framework for visual sentiments analysis. In: IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 219–224 (2019)
5.
Haque, T.U., Saber, N.N., Shah, F.M.: Sentiment analysis on large scale amazon product reviews. In: 2018 IEEE International Conference on Innovative Research and Development (ICIRD), pp. 1–6 (2018)
6.
Zvarevashe, K., Olugbara, O.O.: A framework for sentiment analysis with opinion mining of hotel reviews. In: Conference on Information Communications Technology and Society (ICTAS), pp. 1–4 (2018)
7.
Chandra Pandey, A., Singh Rajpoot, D., Saraswat, M.: Twitter sentiment analysis using hybrid cuckoo search method. Inf. Process. Manag. 53(4), 764–779 (2017)
8.
Suganya, B.: Particle swarm optimization based feature selection and summarization of customer reviews. Int. Conf. Emerg. Trends Eng. Sci. Sustain. Technol., pp. 131–135 (2017)
9.
Bhardwaj, A., Narayan, Y., Vanraj, P., Dutta, M.: Sentiment analysis for Indian stock market prediction using Sensex and nifty. Procedia Comput. Sci. 70, 85–91 (2015)
10.
Kušen, E., Strembeck, M.: Politics, sentiments, and misinformation: an analysis of the Twitter discussion on the 2016 Austrian Presidential Elections. Online Soc. Netw. Media 5, 37–50 (2017)CrossRef
11.
Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from twitter posts. 2012.
12.
Ragini, J.R., Anand, P.M.R., Bhaskar, V.: Big data analytics for disaster response and recovery through sentiment analysis. Int. J. Inf. Manage. 42(May), 13–24 (2018)CrossRef
13.
Christensen, J., Bastien, C.: Introduction to general optimization principles and methods. Nonlinear Optim. Veh. Saf. Struct., pp. 107–168 (2016).
14.
Yang, X.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2010)
15.
Yang, X.S.: Nature-Inspired Optimization Algorithms, pp. 1–263. Elsevier, Amsterdam (2014)MATHCrossRef
16.
Beheshti, Z., Shamsuddin, S.M.: A review of population-based meta-Heuristic algorithm. Int. J. Adv. Soft Comput. Appl. 5, 1 (2013)
17.
Voss, S., Martello, S., Osman, I. H., Roucairol, C.: Meta-heuristics: Advances and trends in local search paradigms for optimization. Springer, New York (2012)
18.
M. Gavrilas, Heuristic and metaheuristic optimization techniques with application to power systems. Proc. 12th WSEAS Int. Conf. Math. Methods Comput. Tech. Electr. Eng., pp. 95–103 (2010)
19.
Fister, I., Yang, X.S., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektroteh. Vestnik/Electrotechnical Rev. 80(3), 116–122 (2013)
20.
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE E International Conference on Neural Networks, pp. 1942–1948 (1995)
21.
Rana, T.A., Cheah, Y.N.: Hybrid rule-based approach for aspect extraction and categorization from customer reviews. Proceedings 2015 9th International Conference IT Asia Transform. Big Data into Knowledge, CITA 2015 (2015)
22.
Li, X., Li, J., Wu, Y.: A global optimization approach to multi-polarity sentiment analysis. PLoS ONE 10(4), 1–18 (2015)
23.
Slowik, A., Kwasnicka, H.: Nature inspired methods and their industry applications—Swarm Intelligence Algorithms. IEEE Trans. Ind. Informatics 1(11), 1–1 (2017)
24.
Yang, B., Chen, Y., Zhao, Z.: Survey on applications of particle swarm optimization in electric power systems. Control Autom. 2007. ICCA 2007. IEEE Int. Conf., vol. 00(3), pp. 481–486 (2007)
25.
Chen, C.-Y., Ye, F.: Particle swarm optimization algorithm and its application to clustering analysis. 2004 IEEE Conf. Netw. Sens. Control 1, pp 789–794 (2004)
26.
Tsai, M.-C. T., Chen, K.-H., Lin, H.-C.: An application of PSO algorithm and decision tree for Medical Problem. 2015 IEEE 15th Int. Conf. Bioinforma. Bioeng. BIBE 2015, pp. 124–126 (2015)
27.
Kristiyanti, D.A., Wahyudi, M.: Feature selection based on genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review. 2017 5th Int. Conf. Cyber IT Serv. Manag. (2017)
28.
Akhtar, M.S., Gupta, D., Ekbal, A., Bhattacharyya, P.: Feature selection and ensemble construction: a two-step method for aspect based sentiment analysis. Knowl. Based Syst. 125, 116–135 (2017)CrossRef
29.
Liu, Z., Liu, S., Liu, L., Sun, J., Peng, X., Wang, T.: Sentiment recognition of online course reviews using multi-swarm optimization-based selected features. Neurocomputing 185, 11–20 (2016)CrossRef
30.
Basari, A.S.H., Hussin, B., Ananta, I.G.P., Zeniarja, J.: Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Eng. 53, 453–462 (2013)CrossRef
31.
Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. Thesis (1992)
32.
Dorigo, M., Thomas, S.: Ant Colony Optimization. Cambridge, MA, USA MIT Press (2004)
33.
Chakraborty, B., Banerjee, S.: Modeling the evolution of post disaster social awareness from social web sites. In: 2013 IEEE International Conference on Cybernetics, pp. 51–56 (2013)
34.
Ahmad, S. R., Yusop, N.M.M., Bakar, A.A., Yaakub, M.R.: Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis. AIP Conf. Proc., vol. 1891 (2017)
35.
Goel, L., Prakash, A.: Sentiment analysis of online communities using swarm intelligence algorithms. 2016 8th Int. Conf. Comput. Intell. Commun. Networks, pp. 330–335 (2016)
36.
Forsati, R., Moayedikia, A., Jensen, R., Shamsfard, M., Meybodi, M.R.: Enriched ant colony optimization and its application in feature selection. Neurocomputing 142, 354–371 (2014)CrossRef
37.
Mavrovouniotis, M., Muller, F.M., Yang, S.: Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Trans. Cybern. 47(7), 1743–1756 (2017)CrossRef
38.
Shirzad, A., Tabesh, M.: Multiobjective optimization of pressure dependent dynamic design for water distribution networks. Water Resour. Manag. 31(9), 2561–2578 (2017)CrossRef
39.
Banerjee, S., Agarwal, N.: Analyzing collective behavior from blogs using swarm intelligence. Knowl. Inf. Syst. 33(3), 523–547 (2012)CrossRef
40.
Deif, D.S., Member, S., Gadallah, Y., Member, S.: An ant colony optimization approach for the deployment of reliable wireless sensor networks. IEEE Access 5, 10744–10756 (2017)CrossRef
41.
Wang, F., Lin, B., Li, X.: An ant particle filter for visual tracking. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 417–422 (2017)
42.
Yang, X.S.: Firefly algorithms for multimodal optimization. Proc. fifth Symp. Stoch. Algorithms, Found. Appl. Lect. Notes Comput. Sci., vol. 5792, pp. 169–178 (2009)
43.
Yang, X.S., He, X.: Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1, 1–36 (2013)CrossRef
44.
Rajput, V.S., Dubey, S.M.: A new approach of firefly algorithm for optimizing reviews of opinion mining. In: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication, pp. 18–23 (2016)
45.
Emary, E., Zawbaa, H.M., Ghany, K.K.A., Hassanien, A.E., Parv, B.: Firefly optimization algorithm for feature selection. In: Proc. 7th Balk. Conf. Informatics Conf. - BCI ’15, pp. 1–7 (2015)
46.
Banati, H., Bajaj, M.: Promoting products online using firefly algorithm. In: 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), vol. 2, pp. 11–21 (2005)
47.
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. World Congr. Nat. Biol. Inspired Comput., pp. 210–214 (2009)
48.
Devi, K.N., Bhaskaran, V.M., Kumar, G.P.: Cuckoo optimized SVM for stock market prediction. ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems (2015)
49.
Redmond, M., Salesi, S., Cosma, G.: A novel approach based on an extended cuckoo search algorithm for the classification of tweets which contain emoticon and Emoji. In 2017 2nd International Conference on Knowledge Engineering and Applications (2017)
50.
Rajamohana, S.P., Umamaheswari, K., Keerthana, S.V.: An effective hybrid Cuckoo Search with Harmony search for review spam detection. in 3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB17), pp. 524–527 (2017)
51.
Yang, X.S.: A new metaheuristic Bat-inspired Algorithm. Nat. Inspired Coop. Strateg. Optim. (NICSO 2010), vol. 284, pp. 65–74 (2010)
52.
Khurana, H., Sahu, S.K.: Bat inspired sentiment analysis of Twitter data. Prog. Adv. Comput. Intell. Eng. 2, 639–650 (2018)CrossRef
53.
Nakamura, R.Y.M., Pereira, L.A.M., Costa, K.A., Rodrigues, D., Papa, J.P., Yang, X.S.: BBA: A binary bat algorithm for feature selection. In: Brazilian Symposium of Computer Graphic and Image Processing, pp. 291–297 (2012)
54.
Fister, I.J., Fister, I., Yang, S.-S., Fong, S., Zhuang, Y.: Bat algorithm: recent advances. Int. Symp. Comput. Intell. Inform. 15(1), 163–167 (2014)
55.
Chakri, A., Khelif, R., Benouaret, M., Yang, X.S.: New directional bat algorithm for continuous optimization problems. Expert Syst. Appl. 69, 159–175 (2017)CrossRef
56.
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetMATHCrossRef
57.
Dhurve, M.R., Prof, A., Seth, M.: A survey on weighted sentiment analysis using artificial bee colony algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 4(4), 1173–1178 (2015)
58.
Sumathi, M., Karthik, T., Marikkannan, S.: Artificial bee colony optimization for feature selection in opinion mining. J. Theor. Appl. Inf. Technol. 66(1), 368–379 (2014)
59.
Ramaswamy, S.: An improved fuzzy classifier using EI ABC. World Appl. Sci. J. 35(1), 33–42 (2017)
60.
Saravanan, T.M., Tamilarasi, A.: Effective sentiment analysis for opinion mining using artificial bee colony optimization. Res. J. Appl. Sci. Eng. Technol. 12(8), 828–840 (2016)CrossRef
61.
Yang, X.S.: Flower pollination algorithm for global optimization. Int. Conf. Unconv. Comput. Nat. Comput. Lect. Notes Comput. Sci., 240–249 (2012)
62.
Rajamohana, S.P., Umamaheswari, K.: A hybrid approach to optimize feature selection process using iBPSO-BFPA for review spam detection. Appl. Math. Inf. Sci. 11(5), 1443–1449 (2017)CrossRef
63.
Kaur, M., Kaur, N.: Text clustering using PBO algorithm for analysis and optimization. Int. J. Curr. Eng. Technol. 4(6), 3876–3878 (2014)
64.
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. Int. J. Comput. Intell. Stud. 1(1), 93–119 (2009)CrossRef
65.
Alboaneen, D.A., Tianfield, H., Zhang, Y.: Sentiment analysis via Multi-layer perceptron trained by meta-heuristic optimisation. In: 2017 IEEE International Conference on Big Data (BIGDATA), pp. 4548–4553 (2017)
66.
Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manag. 129(3), 210–225 (2003)CrossRef
67.
Yuvaraj, N., Sabari, A.: Twitter sentiment classification using binary shuffled frog algorithm. Intell. Autom. Soft Comput. 23(2), 373–381 (2017)CrossRef
68.
Rajamohana, S.P., Umamaheshwari, K., Karthiga, R.: Sentiment analysis using shuffled frog leaping algorithm. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(12), 138–142 (2017)
69.
Nirmala, D.K., Jayanthi, P.: Sentiment classification using SVM and PSO. Int. J. Adv. Eng. Technol. 7(2), 411–413 (2016)
70.
Budhi, G.S., Chiong, R., Hu, Z., Pranata, I., Dhakal, S.: Multi-PSO based classifier selection and parameter optimisation for sentiment polarity prediction. In: 2018 IEEE Conference on Big Data and Analytics, ICBDA, pp. 68–73 (2018)
71.
Gupta, D.K., Reddy, K.S.: PSO-ASent: feature selection using particle swarm optimization for aspect based sentiment analysis. In: Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science, vol. 9103, pp. 220–233 (2015)
72.
Jiang, H., Kwong, C.K., Park, W.Y., Yu, K.M.: A multi-objective PSO approach of mining association rules for affective design based on online customer reviews. J. Eng. Des. 29(7), 381–403 (2018)CrossRef
73.
Rajamohana, S.P., Umamaheswari, K.: An integrated evolutionary algorithm for review spam detection on online reviews. Adv. Nat. Appl. Sci. 10(17), 228–236 (2016)
74.
Sonagi, A., Gore, D.: Efficient sentiment analysis using hybrid PSO-GA approach. Int. J. Innov. Res. Comput. Commun. Eng. 5(2), 1302–1309 (2017)
75.
Souza, E., Oliveira, A.L.I., Oliveira, G., Silva, A., Santos, D.: An unsupervised particle swarm optimization approach for opinion clustering. In: Proceedings-2016 5th Brazilian Conference Intelligence Systems BRACIS 2016, pp. 307–312 (2016)
76.
Ahmad, S.R., Bakar, A.A., Yaakub, M.R.: Ant colony optimization for text feature selection in sentiment analysis. Intell. Data Anal. 23(1), 133–158 (2019)CrossRef
77.
Kaur, J., Sehra, S.S., Sehra, S.K.: Sentiment analysis of Twitter data using hybrid method of support vector machine and ant colony optimization. Int. J. Comput. Sci. Inf. Secur. 14(7), 222–226 (2016)
78.
Ahmad, S.R., Bakar, A.A., Yaakub, M.R., Moziyana, N., Yusop, M.: Statistical validation of ACO-KNN algorithm for sentiment analysis. J. Telecommun. Electron. Comput. Eng. 9(2), 165–170 (2017)
79.
Joseph, A.: Sentiment analysis using CRF and optimal temporal boundary. Indian J. Educ. Inf. Manag. 5(April), 1–8 (2016)
80.
81.
Saravanan, T.M., Tamilarasi, A.: An efficient hierarchical improved relevance vector machine for effective sentiment analysis. Int. J. Comput. Technol. Appl. 10(20), 139–152 (2017)
82.
Rani, A.S.S., Scholar, P.G.: Unsupervised feature selection using binary bat algorithm. In: IEEE Sponsored 2nd International Conference on Electronics and Communication Systems (ICECS 2015), pp. 451–456 (2015)
83.
Palanisamy, S., Kanmani, S.: Artificial bee colony approach for optimizing feature selection. Int. J. Comput. Sci. Issues 9(3), 432–438 (2012)
84.
Orkphol, K., Yang, W.: Sentiment analysis on microblogging with K-means clustering and artificial bee colony. Int. J. Comput. Intell. Appl. 18(2), 1–22 (2019)
85.
Rajamohana, S., Umamaheswari, K., Abirami, B.: Adaptive binary flower pollination algorithm for feature selection in review spam detection, pp. 1–4 (2017)
86.
Wahyudi, M., Kristiyanti, D.A.: Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization. J. Theor. Appl. Inf. Technol. 91(1), 189 (2016)
87.
Idrus, A., Brawijaya, H.: Sentiment analysis of state officials news on online media based on public opinion using naive bayes classifier algorithm and particle swarm optimization. In: 6th International Conference on Cyber and IT Service Management, CITSM 2018, pp. 1–7 (2018)
88.
Umamaheswari, K., Rajamohana, S.P., Aishwaryalakshmi, G.: Opinion mining using hybrid methods. In: International Conference on Innovations in Computing Techniques (ICICT 2015), pp. 18–21 (2015)
89.
Nagarajan, S.M., Gandhi, U.D.: Classifying streaming of Twitter data based on sentiment analysis using hybridization. Neural Comput. Appl. 31(5), 1425–1433 (2019)CrossRef
90.
Jain, A., Pal Nandi, B., Gupta, C., Tayal, D.K.: Senti-NSetPSO: large-sized document-level sentiment analysis using Neutrosophic Set and particle swarm optimization. Soft Comput. 24, 1–13 (2019)
91.
Alfarraj, O., AlZubi, A.A.: A novel approach for ranking customer reviews using a modified PSO-based aspect ranking algorithm. Cluster Comput. 22, 1–7 (2018)
92.
93.
Tubishat, M., Abushariah, M.A.M., Idris, N., Aljarah, I.: Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl. Intell. 49(5), 1688–1707 (2019)CrossRef
94.
Kalarani, P., Selva Brunda, S.: Sentiment analysis by POS and joint sentiment topic features using SVM and ANN. Soft Comput. 23(16), 7067–7079 (2019)CrossRef
95.
Shang, L., Zhou, Z., Liu, X.: Particle swarm optimization-based feature selection in sentiment classification. Soft Comput. 20(10), 3821–3834 (2016)CrossRef
96.
Kurniawati, I., Pardede, H.F.: Hybrid method of information gain and particle swarm optimization for selection of features of SVM-based sentiment analysis. In: 2018 International Conference on Information Technology Systems and Innovation, pp. 1–5 (2019)
97.
Rajamohana, S.P., Umamaheswari, K.: Hybrid optimization algorithm of improved binary particle swarm optimization (iBPSO) and cuckoo search for review spam detection. In: Proceedings on 9th International Conference Machine Learning and Computing - ICMLC 2017, pp. 238–242 (2017)
98.
Sharmila, R., Sivajothi, M.: Eco inspired bees: a novel feature selection mechanism for sentiment analysis. Int. J. Pure Appl. Math. 114(2), 307–327 (2017)CrossRef
99.
Jiang, D., Tao, Q., Wang, Z., Dong, L.: An intelligent logistic regression approach for verb expression’s sentiment analysis. In: Proceedings of Recent Developments in Intelligent Computing, Communication and Devices, vol. 752, pp. 173–181 (2019)
100.
Chiong, R., Adam, M.T.P., Fan, Z., Lutz, B., Hu, Z., Neumann, D.: A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, pp. 278–279 (2018)
101.
Tubishat, M., Idris, N., Abushariah, M.A.M.: Implicit aspect extraction in sentiment analysis: review, taxonomy, oppportunities, and open challenges. Inf. Process. Manag. 54(4), 545–563 (2018)CrossRef
102.
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108, 42–49 (2016)CrossRef
103.
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–239 (2008)
104.
Van De Kauter, M., Breesch, D., Hoste, V.: Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Syst. Appl. 42(11), 4999–5010 (2015)CrossRef
105.
Ansari, O., Zahir, J., Mousannif, H.: Context-based sentiment analysis: a survey. In: International Conference on Model and Data Engineering, Part of the Communications in Computer and Information Science book series, pp. 91–97 (2018)
106.
Katz, G., Ofek, N., Shapira, B.: ConSent: Context-based sentiment analysis. Knowl. Based Syst. 84, 162–178 (2015)CrossRef
107.
Sailunaz, K., Alhajj, R.: Emotion and sentiment analysis from Twitter text. J. Comput. Sci. 36, 101003 (2019)CrossRef
108.
Sailunaz, K., Dhaliwal, M., Rokne, J., Alhajj, R.: Emotion detection from text and speech: a survey. Soc. Netw. Anal. Min. 8(28), 1–26 (2018)
109.
van Hee, C., Lefever, E., Hoste, V.: Exploring the fine-grained analysis and automatic detection of irony on Twitter. Lang. Resour. Eval. 52, 1–25 (2018)CrossRef
110.
Bharti, S.K., Vachha, B., Pradhan, R.K., Babu, K.S., Jena, S.K.: Sarcastic sentiment detection in tweets streamed in real time: a big data approach. Digit. Commun. Netw. 2, 108–121 (2016)CrossRef
111.
Darwish, A.: Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Futur. Comput. Informatics J. 3(2), 231–246 (2018)MathSciNetCrossRef
112.
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRef
113.
Chu, S.-C., Tsai, P., Pan, J.-S.: Cat Swarm Optimization. In: Pacific Rim International Conference Artificial Intelligence Part Lecture Notes in Computer Science B, pp. 854–858 (2006)
114.
Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)
115.
Ghaemi, M., Feizi-Derakhshi, M.R.: Forest optimization algorithm. Expert Syst. Appl. 41(15), 6676–6687 (2014)CrossRef
116.
Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S.F., Pantic, M.: A survey of multimodal sentiment analysis. Image Vis. Comput. 65, 3–14 (2017)CrossRef
117.
Poria, S., Cambria, E., Howard, N., Bin Huang, G., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)CrossRef
118.
Sun, X., Li, C., Ren, F.: Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210, 227–236 (2016)CrossRef
119.
Baly, R., Hajj, H., Habash, N., Shaban, K.B., El-Hajj, W.: A sentiment treebank and morphologically enriched recursive deep models for effective sentiment analysis in Arabic. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 16, 4–21 (2017)CrossRef
120.
Trinh, S., Nguyen, L., Vo, M., Do, P.: Lexicon-based sentiment analysis of facebook comments in Vietnamese language. Recent Dev. Intell. Inf. Database Syst. Part Stud. Comput. Intell. B 642, 263–276 (2016)
121.
Metadaten
Titel
A comparative study on bio-inspired algorithms for sentiment analysis
verfasst von
Ashima Yadav
Dinesh Kumar Vishwakarma
Publikationsdatum
03.02.2020
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 4/2020
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-020-03062-w

Weitere Artikel der Ausgabe 4/2020

Cluster Computing 4/2020 Zur Ausgabe

OriginalPaper

A meta-heuristic based multi objective optimization for load distribution in cloud data center under varying workloads

OriginalPaper

Multi-level particle swarm optimisation and its parallel version for parameter optimisation of ensemble models: a case of sentiment polarity prediction

OriginalPaper

Satisfaction aware QoS-based bidirectional service mapping in cloud environment

OriginalPaper

Performance consequence of user space file systems due to extensive CPU sharing in virtual environment

OriginalPaper

EPBLA: energy-efficient consolidation of virtual machines using learning automata in cloud data centers

OriginalPaper

An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments

Premium Partner

    Bildnachweise