Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Epileptic Seizure Detection Using CNN Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Misogynous Text Classification Using SVM and LSTM

Authors : Maibam Debina Devi, Navanath Saharia

Published in: Advanced Computing

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Discrimination and manipulation are becoming predominant in social network activities. Comments bearing attitudes, such as distress, hate, and aggression in Social Networking Sites (SNS) add fuel to the process of discrimination. This research aims to classify texts, which are misogynous in nature using Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) for user-generated texts of English and Hindi languages written using the Roman script. Approximately 87% accuracy was achieved while SVM was trained with Term Frequency-Inverse Document Frequency (TF-IDF) feature and for Hindi comments approximately 93.43% accuracy was achieved for English using Bidirectional LSTM (Bi-LSTM).

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter SQL Query from Portuguese Language Using Natural Language Processing
next chapter Active Learning Enhanced Sequence Labeling for Aspect Term Extraction in Review Data
Footnotes
5
 
Literature
1.
2.
Ahluwalia, R., Shcherbinina, E., Callow, E., Nascimento, A.C., De Cock, M.: Detecting misogynous tweets. In: IberEval@ SEPLN, pp. 242–248 (2018)
3.
Altın, L.S.M., Bravo, A., Saggion, H.: LaSTUS/TALN at TRAC-2020 trolling, aggression and cyberbullying. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 83–86 (2020)
4.
Arras, L., Montavon, G.G., Müller, K.R., Samek, W.: Explaining recurrent neural network predictions in sentiment analysis. arXiv preprint arXiv:​1706.​07206 (2017)
5.
Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)
6.
Basile, V., et al.: SemEval-2019 task 5: multilingual detection of hate speech against immigrants and women in twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 54–63 (2019)
7.
Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3606–3611 (2019)
8.
Bhattacharya, S., et al.: Developing a multilingual annotated corpus of misogyny and aggression. arXiv preprint arXiv:​2003.​07428 (2020)
9.
10.
Charton, E., Meurs, M.J., Jean-Louis, L., Gagnon, M.: Using collaborative tagging for text classification: from text classification to opinion mining. In: Informatics, vol. 1, pp. 32–51. Multidisciplinary Digital Publishing Institute (2014)
11.
Chekuri, C., Goldwasser, M.H., Raghavan, P., Upfal, E.: Web search using automatic classification. In: Proceedings of the Sixth International Conference on the World Wide Web (1997)
12.
Conneau, A., Schwenk, H., Barrault, L., Lecun, Y.: Very deep convolutional networks for text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1107–1116 (2017)
13.
Corazza, M., Menini, S., Cabrio, E., Tonelli, S., Villata, S.: A multilingual evaluation for online hate speech detection. ACM Trans. Internet Technol. (TOIT) 20(2), 1–22 (2020)CrossRef
14.
Das, A., Gambäck, B.: Code-mixing in social media text: the last language identification frontier? (2015)
15.
Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Eleventh International AAAI Conference on Web and Social Media (2017)
16.
Devi, M.D., Saharia, N.: Learning adaptable approach to classify sentiment with incremental datasets. Procedia Comput. Sci. 171, 2426–2434 (2020)CrossRef
17.
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATH
18.
Frenda, S., Ghanem, B., Montes-y Gómez, M., Rosso, P.: Online hate speech against women: automatic identification of misogyny and sexism on Twitter. J. Intell. Fuzzy Syst. 36(5), 4743–4752 (2019)CrossRef
19.
Goenaga, I., et al.: Automatic misogyny identification using neural networks. In: IberEval@ SEPLN, pp. 249–254 (2018)
20.
Joulin, A., Grave, É., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Short Papers, vol. 2, pp. 427–431 (2017)
21.
Kim, J.: User-generated content (UGC) revolution?: critique of the promise of Youtube. Ph.D. thesis, University of Iowa (2010)
22.
Kim, J.: # iamafeminist as the “mother tag”: feminist identification and activism against misogyny on Twitter in South Korea. Fem. Media Stud. 17(5), 804–820 (2017)CrossRef
23.
Kim, Y., Nam, T.: An efficient text filter for adult web documents. In: 2006 8th International Conference Advanced Communication Technology, vol. 1, pp. 3-pp. IEEE (2006)
24.
Liu, H., Chiroma, F., Cocea, M.: Identification and classification of misogynous tweets using multi-classifier fusion. In: IberEval@ SEPLN, pp. 268–273 (2018)
25.
Lynn, T., Endo, P.T., Rosati, P., Silva, I., Santos, G.L., Ging, D.: A comparison of machine learning approaches for detecting misogynistic speech in urban dictionary. In: 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (CyberSA), pp. 1–8. IEEE (2019)
26.
MacAvaney, S., Yao, H.R., Yang, E., Russell, K., Goharian, N., Frieder, O.: Hate speech detection: challenges and solutions. PloS One 14(8), e0221152 (2019)CrossRef
27.
Mullany, L., Trickett, L.: Misogyny hate crime evaluation report (2018)
28.
29.
Saharia, N.: Phone-based identification of language in code-mixed social network data. J. Stat. Manag. Syst. 20(4), 565–574 (2017)
30.
Schmidt, S., Schnitzer, S., Rensing, C.: Text classification based filters for a domain-specific search engine. Comput. Ind. 78, 70–79 (2016) CrossRef
31.
Shushkevich, E., Cardiff, J.: Automatic misogyny detection in social media: a survey. Computación y Sistemas 23(4) (2019)
32.
Teodorescu, H.N., Saharia, N.: An internet slang annotated dictionary and its use in assessing message attitude and sentiments. In: 2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), pp. 1–8. IEEE (2015)
33.
Teodorescu, H.N., Saharia, N.: A semantic analyzer for detecting attitudes on SNs. In: 2016 International Conference on Communications (COMM), pp. 47–50. IEEE (2016)
34.
Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In: Proceedings of the NAACL Student Research Workshop, pp. 88–93 (2016)
35.
Wiegand, M., Ruppenhofer, J., Kleinbauer, T.: Detection of abusive language: the problem of biased datasets. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Long and Short Papers), vol. 1, pp. 602–608 (2019)
36.
Yao, M., Chelmis, C., Zois, D.S.: Cyberbullying ends here: towards robust detection of cyberbullying in social media. In: The World Wide Web Conference, pp. 3427–3433 (2019)
Metadata
Title
Misogynous Text Classification Using SVM and LSTM
Authors
Maibam Debina Devi
Navanath Saharia
Copyright Year
2021
Publisher
Springer Singapore
Book
Advanced Computing

Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0401-0_26

Premium Partner

    Image Credits