Abstract
A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1 score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results, respectively, represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
References
Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M (2013) Arabic sentiment analysis: Lexicon-based and corpus-based. In: 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE, pp 1–6
Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M, Al-Kabi MN, Al-rifai S (2014a) Towards improving the lexicon-based approach for arabic sentiment analysis. Int J Inf Technol Web Eng (IJITWE) 9(3):55–71
Abdulla N, Mohammed S, Al-Ayyoub M, Al-Kabi M et al (2014b) Automatic lexicon construction for arabic sentiment analysis. In: 2014 international conference on future internet of things and cloud (FiCloud) IEEE. pp 547–552
Abdul-Mageed M, Diab M, Kübler S (2014) Samar: subjectivity and sentiment analysis for arabic social media. Comput Speech Lang 28(1):20–37
Abdul-Mageed M, Diab M (2012a) Toward building a large-scale arabic sentiment lexicon. In: Proceedings of the 6th international global WordNet conference, pp 18–22
Abdul-Mageed M, Diab MT (2012b) Awatif: A multi-genre corpus for modern standard arabic subjectivity and sentiment analysis. In: LREC, Citeseer. pp 3907–3914
Abdul-Mageed M, Diab MT (2016) Sana: alarge scale multi-genre, multi-dialect lexicon for arabic subjectivity and sentiment analysis. In: LREC
Al Shboul B, Al-Ayyoub M, Jararweh Y (2015) Multi-way sentiment classification of Arabic reviews. In: 2015 6th international conference on information and communication systems (ICICS). IEEE, pp 206–211
Alayba AM, Palade V, England M, Iqbal R (2018) A combined cnn and lstm model for arabic sentiment analysis. In: International cross-domain conference for machine learning and knowledge extraction. Springer, New York, pp 179–191
Al-Azani S, El-Alfy ESM (2017) Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text. Procedia Comput Sci 109:359–366
Alowaidi S, Saleh M, Abulnaja O (2017) Semantic sentiment analysis of arabic texts. Int J Adv Comput Sci Appl 8(2):256–262
Al-Sallab A, Baly R, Hajj H, Shaban KB, El-Hajj W, Badaro G (2017) Aroma: a recursive deep learning model for opinion mining in arabic as a low resource language. ACM Trans Asian Low-Resour Lang Inf Process (TALLIP) 16(4):25
Altowayan AA, Tao L (2016) Word embeddings for arabic sentiment analysis. In: 2016 IEEE international conference on big data (big data). IEEE, pp 3820–3825
Altrabsheh N, El-Masri M, Mansour H (2017) Combining sentiment lexicons of arabic terms. In: 23rd Americas Conference on Information Systems
Al-Twairesh N, Al-Khalifa H, Al-Salman A, Al-Ohali Y (2017) Arasenti-tweet: a corpus for arabic sentiment analysis of saudi tweets. Procedia Comput Sci 117:63–72
Aly M, Atiya A (2013) Labr: A large scale arabic book reviews dataset. In: Proceedings of the 51st Annual meeting of the association for computational linguistics, vol 2, Short Papers, pp 494–498
Arora M, Kansal V (2019) Character level embedding with deep convolutional neural network for text normalization of unstructured data for twitter sentiment analysis. Soc Netw Anal Min 9(1):12
Atia S, Shaalan K (2015) Increasing the accuracy of opinion mining in arabic. In: 2015 first international conference on Arabic computational linguistics (ACLing). IEEE, pp 106–113
Attia M, Samih Y, El-Kahky A, Kallmeyer L (2018) Multilingual multi-class sentiment classification using convolutional neural networks. In: LREC
Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10
Badaro G, Baly R, Hajj H, Habash N, El-Hajj W (2014) A large scale arabic sentiment lexicon for arabic opinion mining. In: Proceedings of the EMNLP 2014 workshop on arabic natural language processing (ANLP), pp 165–173
Badaro G, Baly R, Akel R, Fayad L, Khairallah J, Hajj H, Shaban K, El-Hajj W (2015) A light lexicon-based mobile application for sentiment mining of arabic tweets. In: proceedings of the second workshop on Arabic natural language processing, pp 18–25
Banea C, Mihalcea R, Wiebe J (2013) Porting multilingual subjectivity resources across languages. IEEE Trans Affect Comput 4(2):211–225
Barhoumi A, Aloulou YEC, Belguith LH (2017) Document embeddings for arabic sentiment analysis. Language Processing and Knowledge Management 1988
Bisio F, Meda C, Gastaldo P, Zunino R, Cambria E (2016) Sentiment-oriented information retrieval: Affective analysis of documents based on the senticnet framework. In: Sentiment analysis and ontology engineering, pp 175–197. Springer, New York
Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 440–447
Boudad N, Faizi R, Thami ROH, Chiheb R (2017) Sentiment analysis in Arabic: a review of the literature. Ain Shams Eng J 9:228
Buckwalter T (2004) Buckwalter arabic morphological analyzer version 2.0. linguistic data consortium, university of pennsylvania, 2002. ldc cat alog no.: Ldc2004l02. Technical report, ISBN 1-58563-324-0
Cambria E, Speer R, Havasi C, Hussain A (2010) Senticnet: A publicly available semantic resource for opinion mining. In: AAAI fall symposium: commonsense knowledge, 10
Cambria E, Hussain A, Vinciarelli A (2017) Affective reasoning for big social data analysis. IEEE Trans Affect Comput 8(4):426–427
Chen H, Sun M, Tu C, Lin Y, Liu Z (2016) Neural sentiment classification with user and product attention. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1650–1659
Cramer JS (2002) The origins of logistic regression
Dahou A, Xiong S, Zhou J, Haddoud MH, Duan P (2016) Word embeddings and convolutional neural network for arabic sentiment classification. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 2418–2427
Denecke K (2008) Using sentiwordnet for multilingual sentiment analysis. In: IEEE 24th international conference on data engineering workshop, 2008. ICDEW 2008. IEEE, pp 507–512
Diab MT, Al-Badrashiny M, Aminian M, Attia M (2014) Tharwa: A large scale dialectal arabic-standard arabic-english lexicon. In: LREC
Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 international conference on web search and data mining, ACM. pp 231–240
Dou ZY (2017) Capturing user and product information for document level sentiment analysis with deep memory network. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 521–526
Dou Z, Wei W, Wan X (2018) Improving word embeddings for antonym detection using thesauri and sentiwordnet. In: CCF international conference on natural language processing and Chinese computing. Springer, New York, pp 67–79
El-Beltagy SR (2016a) Niletmrg at semeval-2016 task 7: deriving prior polarities for arabic sentiment terms. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 486–490
El-Beltagy SR (2016b) Nileulex: a phrase and word level sentiment lexicon for egyptian and modern standard arabic. In: LREC
El-Beltagy SR (2017) Weighted nileulex: a scored arabic sentiment lexicon for improved sentiment analysis. In: Language processing, pattern recognition and intelligent systems. special issue on computational linguistics, speech & image processing for Arabic language. World Scientific Publishing Co
El-Beltagy SR, Ali A (2013) Open issues in the sentiment analysis of arabic social media: a case study. In: 2013 9th international conference on innovations in information technology (IIT). IEEE, pp 215–220
El Mahdaouy A, Gaussier E, El Alaoui SO (2016) Arabic text classification based on word and document embeddings. In: International conference on advanced intelligent systems and informatics. Springer, New York, pp 32–41
El-Kilany A, Azzam A, El-Beltagy SR (2018) Using deep neural networks for extracting sentiment targets in arabic tweets. In: Intelligent natural language processing: trends and applications. Springer, New York, pp 3–15
ElSahar H, El-Beltagy SR (2014) A fully automated approach for arabic slang lexicon extraction from microblogs. In: International conference on intelligent text processing and computational linguistics. Springer, New York, pp 79–91
ElSahar H, El-Beltagy SR (2015) Building large arabic multi-domain resources for sentiment analysis. In: International conference on intelligent text processing and computational linguistics. Springer, New York, pp 23–34
Eskander R, Rambow O (2015) Slsa: A sentiment lexicon for standard arabic. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2545–2550
Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. Evaluation 17:1–26
Farghaly A, Shaalan K (2009) Arabic natural language processing: challenges and solutions. ACM Trans Asian Lang Inf Process (TALIP) 8(4):14
Farra N, McKeown K (2017) Smarties: Sentiment models for arabic target entities. arXiv preprint arXiv:1701.03434
Fellbaum C, Alkhalifa M, Black W, Elkateb S, Pease A, Rodriguez H, Vossen P (2006) Introducing the arabic wordnet project. In: Proceedings of the 3rd Global wordnet conference, Jeju Island, Korea, South Jeju, January 22–26, 2006
Fukushima K (1979) Neural network model for a mechanism of pattern recognition unaffected by shift in position-neocognitron. IEICE Tech Rep A 62(10):658–665
Gamal D, Alfonse M, El-Horbaty ESM, Salem ABM (2019) Twitter benchmark dataset for arabic sentiment analysis. Int J Modern Educ Comput Sci 11(1):33
Gatti L, Guerini M, Turchi M (2016) Sentiwords: deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Trans Affect Comput 7(4):409–421
Gilbert B, Hussein J, Hazem H, Wassim EH, Nizar H (2018) Arsel: A large scale arabic sentiment and emotion lexicon. In: OSACT 3: The 3rd Workshop on Open-Source Arabic Corpora and Processing Tools
Graff D, Buckwalter T, Jin H, Maamouri M (2006) Lexicon development for varieties of spoken colloquial arabic. In: LREC
Guellil I, Azouaou F (2016) Arabic dialect identification with an unsupervised learning (based on a lexicon). application case: Algerian dialect. In: 2016 IEEE Intl conference on computational science and engineering (CSE) and IEEE Intl conference on embedded and ubiquitous computing (EUC) and 15th Intl symposium on distributed computing and applications for business engineering (DCABES). IEEE, pp 724–731
Guellil I, Boukhalfa K (2015) Social big data mining: A survey focused on opinion mining and sentiments analysis. In: 2015 12th international symposium on programming and systems (ISPS). IEEE, pp 1–10
Guellil I, Azouaou F, Saâdane H, Semmar N (2017) Une approche fondée sur les lexiques d’analyse de sentiments du dialecte algérien
Guellil I, Azouaou F, Benali F, Hachani AE, Saadane H (2018a) Approche Hybride pour la translitération de l’arabizi algérien: une étude préliminaire. In: Proceedings of the 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN). Rennes, France
Guellil I, Adeel A, Azouaou F, Hussain A (2018b) Sentialg: Automated corpus annotation for algerian sentiment analysis. arXiv preprint arXiv:1808.05079
Guellil I, Azouaou F, Mendoza M (2019a) Arabic sentiment analysis: studies, resources, and tools. Soc Netw Anal Min 9(1):56
Guellil I, Azouaou F, Valitutti A (2019b) English vs arabic sentiment analysis: A survey presenting 100 work studies, resources and tools. In: 2019 IEEE/ACS 16th international conference on computer systems and applications (AICCSA), pp 1–8. IEEE
Habash NY (2010) Introduction to arabic natural language processing. Synth Lect Hum Lang Technol 3(1):1–187
Hamouda A, Rohaim M (2011) Reviews classification using sentiwordnet lexicon. In: World congress on computer science and information technology. sn
Harrat S, Meftouh K, Abbas M, Smaili K (2014) Building resources for algerian arabic dialects. In: Fifteenth annual conference of the international speech communication association
Harrat S, Meftouh K, Smaïli K (2017) Machine translation for arabic dialects (survey). Inf Process Manag 56(2):262–273
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hogenboom A, Bal D, Frasincar F, Bal M, de Jong F, Kaymak U (2013) Exploiting emoticons in sentiment analysis. In: Proceedings of the 28th annual ACM symposium on applied computing. ACM, pp 703–710
Htait A, Fournier S, Bellot P (2017) Lsis at semeval-2017 task 4: Using adapted sentiment similarity seed words for english and arabic tweet polarity classification. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), pp 718–722
Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759
Keyvanpour M, Zandian ZK, Heidarypanah M (2020) Omlml: a helpful opinion mining method based on lexicon and machine learning in social networks. Soc Netw Anal Min 10(1):1–17
Khan FH, Qamar U, Bashir S (2017) A semi-supervised approach to sentiment analysis using revised sentiment strength based on sentiwordnet. Knowl Inf Syst 51(3):851–872
Khoja S, Garside R (1999) Stemming arabic text. Lancaster. Computing Department, Lancaster University, UK
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Kumar A, Kohail S, Kumar A, Ekbal A, Biemann C (2016) Iit-tuda at semeval-2016 task 5: Beyond sentiment lexicon: Combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 1129–1135
Kwaik KA, Saad M, Chatzikyriakidis S, Dobnik S (2018) Shami: a corpus of levantine arabic dialects. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)
Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167
Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies-volume 1, pp 142–150. Association for Computational Linguistics
Mahyoub FH, Siddiqui MA, Dahab MY (2014) Building an arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci 26(4):417–424
Mataoui M, Zelmati O, Boumechache M (2016) A proposed lexicon-based sentiment analysis approach for the vernacular algerian arabic. Res Comput Sci 110:55–70
McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems. ACM, pp 165–172
Medhaffar S, Bougares F, Esteve Y, Hadrich-Belguith L (2017) Sentiment analysis of tunisian dialects: Linguistic resources and experiments. In: Proceedings of the third Arabic natural language processing workshop, pp 55–61
Meftouh K, Harrat S, Jamoussi S, Abbas M, Smaili K (2015) Machine translation experiments on padic: A parallel arabic dialect corpus. In: The 29th Pacific Asia conference on language, information and computation
Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S et al (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235–1241
Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, 3111–3119
Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41
Mohammad S, Salameh M, Kiritchenko S (2016a) Sentiment lexicons for arabic social media. In: LREC
Mohammad SM, Salameh M, Kiritchenko S (2016b) How translation alters sentiment. J Artif Intell Res 55:95–130
Mohammed A, Kora R (2019) Deep learning approaches for arabic sentiment analysis. Soc Netw Anal Min 9(1):52
Mourad A, Darwish K (2013) Subjectivity and sentiment analysis of modern standard arabic and arabic microblogs. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 55–64
Nabil M, Aly M, Atiya A (2015) Astd: Arabic sentiment tweets dataset. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2515–2519
Nagamanjula R, Pethalakshmi A (2020) A novel framework based on bi-objective optimization and LAN2FIS for twitter sentiment analysis. Soc Netw Anal Min 10(1):34
Oghina A, Breuss M, Tsagkias M, de Rijke M (2012) Predicting imdb movie ratings using social media. In: European conference on information retrieval, pp 503–507. Springer, New York
Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. LREc 10(10)
Panos A, Dellaportas P, Titsias MK (2018) Fully scalable gaussian processes using subspace inducing inputs. arXiv preprint arXiv:1807.02537
Poria S, Gelbukh A, Cambria E, Yang P, Hussain A, Durrani T (2012) Merging senticnet and wordnet-affect emotion lists for sentiment analysis. In: 2012 IEEE 11th international conference on signal processing (ICSP). IEEE, vol. 2, pp 1251–1255
Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhyay S (2013) Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intell Syst 28:31–38
Rahab H, Zitouni A, Djoudi M (2017) Siaac: Sentiment polarity identification on arabic algerian newspaper comments. In: Proceedings of the computational methods in systems and software. Springer, New York, pp 139–149
Rahab H, Zitouni A, Djoudi M (2019) SANA: sentiment analysis on newspapers comments in algeria. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.04.012
Ren F, Matsumoto K (2016) Semi-automatic creation of youth slang corpus and its application to affective computing. IEEE Trans Affect Comput 7(2):176–189
Rushdi-Saleh M, Martín-Valdivia MT, Ureña-López LA, Perea-Ortega JM (2011a) Bilingual experiments with an Arabic–English corpus for opinion mining. Proc Int Conf Recent Adv Nat Lang Process 2011:740–745
Rushdi-Saleh M, Martín-Valdivia MT, Ureña-López LA, Perea-Ortega JM (2011b) OCA: opinion corpus for arabic. J Assoc Inf Sci Technol 62(10):2045–2054
Saadane H, Habash N (2015) A conventional orthography for algerian arabic. In: ANLP workshop 2015
Saadane H, Seffih H, Fluhr C, Choukri K, Semmar N (2018) Automatic identification of maghreb dialects using a dictionary-based approach. In: LREC
Sadat F, Mallek F, Boudabous M, Sellami R, Farzindar A (2014) Collaboratively constructed linguistic resources for language variants and their exploitation in nlp application–the case of tunisian arabic and the social media. In: Proceedings of workshop on Lexical and grammatical resources for language processing, pp 102–110
Salameh M, Mohammad S, Kiritchenko S (2015) Sentiment after translation: A case-study on arabic social media posts. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 767–777
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Schmitt M, Steinheber S, Schreiber K, Roth B (2018) Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. arXiv preprint arXiv:1808.09238
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307
Tafreshi S, Diab M (2018) Emotion detection and classification in a multigenre corpus with joint multi-task deep learning. In: Proceedings of the 27th international conference on computational linguistics, pp 2905–2913
Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Long Papers), vol 1, pp 1555–1565
Tellez ES, Miranda-Jiménez S, Graff M, Moctezuma D, Suárez RR, Siordia OS (2017) A simple approach to multilingual polarity classification in twitter. Pattern Recogn Lett 94:68–74
Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544–2558
Tofighy S, Fakhrahmad SM (2018) A proposed scheme for sentiment analysis: effective feature reduction based on statistical information of sentiwordnet. Kybernetes 47(5):957–984
Tomar DS, Sharma P (2016) A text polarity analysis using sentiwordnet based an algorithm. Int J Comput Sci Inf Technol (IJCSIT) 7(1):190–193
Vapnik V (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
Vo DT, Zhang Y (2016) Don’t count, predict! an automatic approach to learning sentiment lexicons for short text. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Short Papers), vol 2, pp 219–224
Wiebe J, Wilson T, Cardie C (2005) Annotating expressions of opinions and emotions in language. Lang Resour Eval 39(2–3):165–210
Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) Opinionfinder: a system for subjectivity analysis. In: Proceedings of hlt/emnlp on interactive demonstrations. Association for Computational Linguistics, pp 34–35
Xia R, Jiang J, He H (2017) Distantly supervised lifelong learning for large-scale social media sentiment analysis. IEEE Trans Affect Comput 8(4):480–491
Yadav P, Pandya D (2017) Sentireview: Sentiment analysis based on text and emoticons. In: 2017 international conference on innovative mechanisms for industry applications (ICIMIA). IEEE, pp 467–472
Zaidan OF, Callison-Burch C (2014) Arabic dialect identification. Comput Linguist 40(1):171–202
Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. In: Advances in neural information processing systems, pp 649–657
Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdisc Rev: Data Min KnowlDisc 8(4):e1253
Zhou ZH, Feng J (2017) Deep forest: Towards an alternative to deep neural networks. arXiv preprint arXiv:1702.08835
Zhou X, Wan X, Xiao J (2016) Attention-based lstm network for cross-lingual sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 247–256
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Guellil, I., Azouaou, F. & Chiclana, F. ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages. Soc. Netw. Anal. Min. 10, 75 (2020). https://doi.org/10.1007/s13278-020-00688-x
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-020-00688-x