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Erschienen in: Artificial Intelligence Review 2/2019

12.12.2018

A journey of Indian languages over sentiment analysis: a systematic review

verfasst von: Sujata Rani, Parteek Kumar

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2019

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Abstract

In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis. This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically. The current status of Indian languages in sentiment analysis is classified according to the Indian language families. The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described. Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented. This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions. In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools. Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.

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Literatur
Zurück zum Zitat Akhtar MS, Ekbal A, Bhattacharyya P (2016a) Aspect based sentiment analysis: category detection and sentiment classification for Hindi. In: 17th International conference on intelligent text processing and computational linguistics, pp 1–12 Akhtar MS, Ekbal A, Bhattacharyya P (2016a) Aspect based sentiment analysis: category detection and sentiment classification for Hindi. In: 17th International conference on intelligent text processing and computational linguistics, pp 1–12
Zurück zum Zitat Akhtar MS, Ekbal A, Bhattacharyya P (2016b) Aspect based sentiment analysis in Hindi: resource creation and evaluation. In: Proceedings of the 10th international conference on language resources and evaluation, pp 1–7 Akhtar MS, Ekbal A, Bhattacharyya P (2016b) Aspect based sentiment analysis in Hindi: resource creation and evaluation. In: Proceedings of the 10th international conference on language resources and evaluation, pp 1–7
Zurück zum Zitat Akhtar MS, Kumar A, Ekbal A, Bhattacharyya P (2016c) A hybrid deep learning architecture for sentiment analysis. In: Proceedings of the 26th international conference on computational linguistics, pp 482–493 Akhtar MS, Kumar A, Ekbal A, Bhattacharyya P (2016c) A hybrid deep learning architecture for sentiment analysis. In: Proceedings of the 26th international conference on computational linguistics, pp 482–493
Zurück zum Zitat Anagha M, Kumar RR, Sreetha K, Rajeev R, Raj PR (2014) Lexical resource based hybrid approach for cross domain sentiment analysis in Malayalam. Int J Eng Sci 15:18–21 Anagha M, Kumar RR, Sreetha K, Rajeev R, Raj PR (2014) Lexical resource based hybrid approach for cross domain sentiment analysis in Malayalam. Int J Eng Sci 15:18–21
Zurück zum Zitat Anagha M, Kumar RR, Sreetha K, Raj PR (2015) Fuzzy logic based hybrid approach for sentiment analysisl of malayalam movie reviews. In: International conference on signal processing. Informatics, communication and energy systems. IEEE, pp 1–4 Anagha M, Kumar RR, Sreetha K, Raj PR (2015) Fuzzy logic based hybrid approach for sentiment analysisl of malayalam movie reviews. In: International conference on signal processing. Informatics, communication and energy systems. IEEE, pp 1–4
Zurück zum Zitat Arora P (2013) Sentiment analysis for Hindi language. MS by Research in Computer Science Arora P (2013) Sentiment analysis for Hindi language. MS by Research in Computer Science
Zurück zum Zitat Arora P, Kaur B (2015) Sentiment analysis of political reviews in Punjabi language. Int J Comput Appl 126(14):1–4 Arora P, Kaur B (2015) Sentiment analysis of political reviews in Punjabi language. Int J Comput Appl 126(14):1–4
Zurück zum Zitat Asghar MZ, Khan A, Khan F, Kundi FM (2018a) Rift: a rule induction framework for twitter sentiment analysis. Arab J Sci Eng 43(2):857–877CrossRef Asghar MZ, Khan A, Khan F, Kundi FM (2018a) Rift: a rule induction framework for twitter sentiment analysis. Arab J Sci Eng 43(2):857–877CrossRef
Zurück zum Zitat Asghar MZ, Kundi FM, Ahmad S, Khan A, Khan F (2018b) T-saf: Twitter sentiment analysis framework using a hybrid classification scheme. Expert Syst 35(1):1–19CrossRef Asghar MZ, Kundi FM, Ahmad S, Khan A, Khan F (2018b) T-saf: Twitter sentiment analysis framework using a hybrid classification scheme. Expert Syst 35(1):1–19CrossRef
Zurück zum Zitat Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Proc Lang Resour Eval 10:2200–2204 Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Proc Lang Resour Eval 10:2200–2204
Zurück zum Zitat Bakliwal A, Arora P, Varma V (2012) Hindi subjective lexicon: a lexical resource for Hindi polarity classification. In: Proceedings of the eight international conference on language resources and evaluation, pp 1189–1196 Bakliwal A, Arora P, Varma V (2012) Hindi subjective lexicon: a lexical resource for Hindi polarity classification. In: Proceedings of the eight international conference on language resources and evaluation, pp 1189–1196
Zurück zum Zitat Balamurali A, Joshi A, Bhattacharyya P (2012) Cross-lingual sentiment analysis for Indian languages using linked Wordnets. In: Proceedings of 24th international conference on computational linguistics: posters, pp 73–82 Balamurali A, Joshi A, Bhattacharyya P (2012) Cross-lingual sentiment analysis for Indian languages using linked Wordnets. In: Proceedings of 24th international conference on computational linguistics: posters, pp 73–82
Zurück zum Zitat Bansal N, Ahmed UZ, Mukherjee A (2013) Sentiment analysis in Hindi. Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India, pp 1–10 Bansal N, Ahmed UZ, Mukherjee A (2013) Sentiment analysis in Hindi. Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India, pp 1–10
Zurück zum Zitat Bhattacharyya P (2017) Indowordnet. In: The WordNet in Indian languages. Springer, pp 1–18 Bhattacharyya P (2017) Indowordnet. In: The WordNet in Indian languages. Springer, pp 1–18
Zurück zum Zitat Chaudhari CV, Khaire AV, Murtadak RR, Sirsulla KS (2017) Sentiment analysis in Marathi using Marathi WordNet. Imp J Interdiscip Res 3(4):1253–1256 Chaudhari CV, Khaire AV, Murtadak RR, Sirsulla KS (2017) Sentiment analysis in Marathi using Marathi WordNet. Imp J Interdiscip Res 3(4):1253–1256
Zurück zum Zitat Das A, Bandyopadhyay S (2010a) Phrase-level polarity identification for Bangla. Int J Comput Linguist Appl 1(1–2):169–182 Das A, Bandyopadhyay S (2010a) Phrase-level polarity identification for Bangla. Int J Comput Linguist Appl 1(1–2):169–182
Zurück zum Zitat Das A, Bandyopadhyay S (2010b) Sentiwordnet for Bangla. Knowl Shar Event Task 2:1–9 Das A, Bandyopadhyay S (2010b) Sentiwordnet for Bangla. Knowl Shar Event Task 2:1–9
Zurück zum Zitat Das A, Bandyopadhyay S (2010c) Sentiwordnet for Indian languages. In: Asian federation for natural language processing, pp 56–63 Das A, Bandyopadhyay S (2010c) Sentiwordnet for Indian languages. In: Asian federation for natural language processing, pp 56–63
Zurück zum Zitat Deepamala N, Kumar R (2015) Polarity detection of Kannada documents. In: International advance computing conference. IEEE, pp 764–767 Deepamala N, Kumar R (2015) Polarity detection of Kannada documents. In: International advance computing conference. IEEE, pp 764–767
Zurück zum Zitat Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. In: International conference on language resources and evaluation, pp 1–26 Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. In: International conference on language resources and evaluation, pp 1–26
Zurück zum Zitat Fondekar A, Pawar JD, Karmali R (2016) Konkani sentiwordnet: resource for sentiment analysis using supervised learning approach. In: Workshop on Indian language data: resources and evaluation (WILDRE3), Portoroz, Slovenia, pp 55–59 Fondekar A, Pawar JD, Karmali R (2016) Konkani sentiwordnet: resource for sentiment analysis using supervised learning approach. In: Workshop on Indian language data: resources and evaluation (WILDRE3), Portoroz, Slovenia, pp 55–59
Zurück zum Zitat Ghosal T, Das SK, Bhattacharjee S (2015) Sentiment analysis on (Bengali horoscope) corpus. In: Annual India conference (INDICON). IEEE, pp 1–6 Ghosal T, Das SK, Bhattacharjee S (2015) Sentiment analysis on (Bengali horoscope) corpus. In: Annual India conference (INDICON). IEEE, pp 1–6
Zurück zum Zitat Govindan R, Haroon RP (2016) A survey on sentiment and emotion classification in Indo-Dravidian languages. Imp J Interdiscip Res 3(1):1040–1042 Govindan R, Haroon RP (2016) A survey on sentiment and emotion classification in Indo-Dravidian languages. Imp J Interdiscip Res 3(1):1040–1042
Zurück zum Zitat Gupta CP, Bal BK (2015) Detecting sentiment in Nepali texts: a bootstrap approach for sentiment analysis of texts in the Nepali language. In: International conference on cognitive computing and information processing. IEEE, pp 1–4 Gupta CP, Bal BK (2015) Detecting sentiment in Nepali texts: a bootstrap approach for sentiment analysis of texts in the Nepali language. In: International conference on cognitive computing and information processing. IEEE, pp 1–4
Zurück zum Zitat Hasan KA, Rahman M et al (2014) Sentiment detection from Bangla text using contextual valency analysis. In: 17th International conference on computer and information technology. IEEE, pp 292–295 Hasan KA, Rahman M et al (2014) Sentiment detection from Bangla text using contextual valency analysis. In: 17th International conference on computer and information technology. IEEE, pp 292–295
Zurück zum Zitat Hassan A, Amin MR, Al Azad AK, Mohammed N (2016) Sentiment analysis on Bangla and Romanized Bangla text using deep recurrent models. In: International workshop on computational intelligence. IEEE, pp 51–56 Hassan A, Amin MR, Al Azad AK, Mohammed N (2016) Sentiment analysis on Bangla and Romanized Bangla text using deep recurrent models. In: International workshop on computational intelligence. IEEE, pp 51–56
Zurück zum Zitat Hegde Y, Padma S (2015) Sentiment analysis for Kannada using mobile product reviews: a case study. In: International on advance computing conference. IEEE, pp 822–827 Hegde Y, Padma S (2015) Sentiment analysis for Kannada using mobile product reviews: a case study. In: International on advance computing conference. IEEE, pp 822–827
Zurück zum Zitat Hegde Y, Padma S (2017) Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In: 7th international on advance computing conference. IEEE, pp 777–782 Hegde Y, Padma S (2017) Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In: 7th international on advance computing conference. IEEE, pp 777–782
Zurück zum Zitat Jayan P, Nair DS, Elizabeth Jisha S (2015) A subjective feature extraction for sentiment analysis in Malayalam language. Int J Eng Sci 14:1–4 Jayan P, Nair DS, Elizabeth Jisha S (2015) A subjective feature extraction for sentiment analysis in Malayalam language. Int J Eng Sci 14:1–4
Zurück zum Zitat Jena MK, Chandra BR (2014) Opinion mining for online Oriya text. Eur J Acad Essays 44–48 Jena MK, Chandra BR (2014) Opinion mining for online Oriya text. Eur J Acad Essays 44–48
Zurück zum Zitat Jha V, Manjunath N, Shenoy PD, Venugopal K, Patnaik LM (2015) Homs: Hindi opinion mining system. In: 2nd International conference on recent trends in information systems. IEEE, pp 366–371 Jha V, Manjunath N, Shenoy PD, Venugopal K, Patnaik LM (2015) Homs: Hindi opinion mining system. In: 2nd International conference on recent trends in information systems. IEEE, pp 366–371
Zurück zum Zitat Joshi A, Balamurali A, Bhattacharyya P (2010) A fall-back strategy for sentiment analysis in Hindi: a case study. In: Proceedings of the 8th international conference on natural language processing, pp 1–6 Joshi A, Balamurali A, Bhattacharyya P (2010) A fall-back strategy for sentiment analysis in Hindi: a case study. In: Proceedings of the 8th international conference on natural language processing, pp 1–6
Zurück zum Zitat Kaur A, Gupta V (2014a) N-gram based approach for opinion mining of Punjabi text. In: International workshop on multi-disciplinary trends in artificial intelligence. Springer, pp 81–88 Kaur A, Gupta V (2014a) N-gram based approach for opinion mining of Punjabi text. In: International workshop on multi-disciplinary trends in artificial intelligence. Springer, pp 81–88
Zurück zum Zitat Kaur A, Gupta V (2014b) Proposed algorithm of sentiment analysis for Punjabi text. J Emerg Technol Web Intell 6(2):180–183 Kaur A, Gupta V (2014b) Proposed algorithm of sentiment analysis for Punjabi text. J Emerg Technol Web Intell 6(2):180–183
Zurück zum Zitat Kaur J, Saini JR (2014) A study and analysis of opinion mining research in Indo-Aryan, Dravidian and Tibeto-Burman language families. Int J Data Min Emerg Technol 4(2):53–60CrossRef Kaur J, Saini JR (2014) A study and analysis of opinion mining research in Indo-Aryan, Dravidian and Tibeto-Burman language families. Int J Data Min Emerg Technol 4(2):53–60CrossRef
Zurück zum Zitat Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE technical report 2 Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. EBSE technical report 2
Zurück zum Zitat Kumar A, Kohail S, Ekbal A, Biemann C (2015a) Iit-tuda: system for sentiment analysis in Indian languages using lexical acquisition. In: International conference on mining intelligence and knowledge exploration. Springer, pp 684–693 Kumar A, Kohail S, Ekbal A, Biemann C (2015a) Iit-tuda: system for sentiment analysis in Indian languages using lexical acquisition. In: International conference on mining intelligence and knowledge exploration. Springer, pp 684–693
Zurück zum Zitat Kumar KA, Rajasimha N, Reddy M, Rajanarayana A, Nadgir K (2015b) Analysis of users sentiments from Kannada web documents. Procedia Comput Sci 54:247–256CrossRef Kumar KA, Rajasimha N, Reddy M, Rajanarayana A, Nadgir K (2015b) Analysis of users sentiments from Kannada web documents. Procedia Comput Sci 54:247–256CrossRef
Zurück zum Zitat Kumar SS, Premjith B, Kumar MA, Soman K (2015c) Amrita\_cen-nlp@ sail2015: sentiment analysis in Indian language using regularized least square approach with randomized feature learning. In: International conference on mining intelligence and knowledge exploration. Springer, pp 671–683 Kumar SS, Premjith B, Kumar MA, Soman K (2015c) Amrita\_cen-nlp@ sail2015: sentiment analysis in Indian language using regularized least square approach with randomized feature learning. In: International conference on mining intelligence and knowledge exploration. Springer, pp 671–683
Zurück zum Zitat Miranda DT, Mascarenhas M (2016) Kop: an opinion mining system in Konkani. In: International conference on recent trends in electronics. Information and communication technology. IEEE, pp 702–705 Miranda DT, Mascarenhas M (2016) Kop: an opinion mining system in Konkani. In: International conference on recent trends in electronics. Information and communication technology. IEEE, pp 702–705
Zurück zum Zitat Mittal N, Agarwal B, Chouhan G, Bania N, Pareek P (2013) Sentiment analysis of Hindi review based on negation and discourse relation. In: Proceedings of international joint conference on natural language processing, pp 45–50 Mittal N, Agarwal B, Chouhan G, Bania N, Pareek P (2013) Sentiment analysis of Hindi review based on negation and discourse relation. In: Proceedings of international joint conference on natural language processing, pp 45–50
Zurück zum Zitat Mukhtar N, Khan MA (2017) Urdu sentiment analysis using supervised machine learning approach. Int J Pattern Recogn Artif Intell 32(02):1–15MathSciNet Mukhtar N, Khan MA (2017) Urdu sentiment analysis using supervised machine learning approach. Int J Pattern Recogn Artif Intell 32(02):1–15MathSciNet
Zurück zum Zitat Mukhtar N, Khan MA, Chiragh N (2017) Effective use of evaluation measures for the validation of best classifier in Urdu sentiment analysis. Cogn Comput 9(4):446–456CrossRef Mukhtar N, Khan MA, Chiragh N (2017) Effective use of evaluation measures for the validation of best classifier in Urdu sentiment analysis. Cogn Comput 9(4):446–456CrossRef
Zurück zum Zitat Mukhtar N, Khan MA, Chiragh N (2018a) Lexicon based approach outperforms supervised machine learning approach for Urdu sentiment analysis in multiple domains. Telemat Inform 35(8):2173–2183CrossRef Mukhtar N, Khan MA, Chiragh N (2018a) Lexicon based approach outperforms supervised machine learning approach for Urdu sentiment analysis in multiple domains. Telemat Inform 35(8):2173–2183CrossRef
Zurück zum Zitat Mukhtar N, Khan MA, Chiragh N, Nazir S (2018b) Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis. Expert Syst 35(6):1–12CrossRef Mukhtar N, Khan MA, Chiragh N, Nazir S (2018b) Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis. Expert Syst 35(6):1–12CrossRef
Zurück zum Zitat Mukku SS, Choudhary N, Mamidi R (2016) Enhanced sentiment classification of Telugu text using ml techniques. In: SAAIP@ 25th international joint conference on artificial intelligence, pp 29–34 Mukku SS, Choudhary N, Mamidi R (2016) Enhanced sentiment classification of Telugu text using ml techniques. In: SAAIP@ 25th international joint conference on artificial intelligence, pp 29–34
Zurück zum Zitat Naidu R, Bharti SK, Babu KS, Mohapatra RK (2017) Sentiment analysis using Telugu sentiwordnet. In: International conference on wireless communications signal processing and networking, pp 1–5 Naidu R, Bharti SK, Babu KS, Mohapatra RK (2017) Sentiment analysis using Telugu sentiwordnet. In: International conference on wireless communications signal processing and networking, pp 1–5
Zurück zum Zitat Nair DS, Jayan JP, Sherly E et al (2014) Sentima-sentiment extraction for Malayalam. In: International conference on advances in computing, communications and informatics. IEEE, pp 1719–1723 Nair DS, Jayan JP, Sherly E et al (2014) Sentima-sentiment extraction for Malayalam. In: International conference on advances in computing, communications and informatics. IEEE, pp 1719–1723
Zurück zum Zitat Nair DS, Jayan JP, Rajeev R, Sherly E (2015) Sentiment analysis of Malayalam film review using machine learning techniques. In: International conference on advances in computing, communications and informatics. IEEE, pp 2381–2384 Nair DS, Jayan JP, Rajeev R, Sherly E (2015) Sentiment analysis of Malayalam film review using machine learning techniques. In: International conference on advances in computing, communications and informatics. IEEE, pp 2381–2384
Zurück zum Zitat Nivedhitha E, Sanjay S, Anand Kumar M, Soman K (2016) Unsupervised word embedding based polarity detection for Tamil tweets. Int J Comput Technol Appl 9(10):4631–4638 Nivedhitha E, Sanjay S, Anand Kumar M, Soman K (2016) Unsupervised word embedding based polarity detection for Tamil tweets. Int J Comput Technol Appl 9(10):4631–4638
Zurück zum Zitat Nongmeikapam K, Khangembam D, Hemkumar W, Khuraijam S, Bandyopadhyay S (2014) Verb based manipuri sentiment analysis. Int J Nat Lang Comput 3(3):113–118CrossRef Nongmeikapam K, Khangembam D, Hemkumar W, Khuraijam S, Bandyopadhyay S (2014) Verb based manipuri sentiment analysis. Int J Nat Lang Comput 3(3):113–118CrossRef
Zurück zum Zitat Pandey P, Govilkar S (2015) A framework for sentiment analysis in Hindi using HSWN. Int J Comput Appl 119(19):23–26 Pandey P, Govilkar S (2015) A framework for sentiment analysis in Hindi using HSWN. Int J Comput Appl 119(19):23–26
Zurück zum Zitat Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Found Trends Inf RetR 2(1–2):1–135CrossRef Pang B, Lee L et al (2008) Opinion mining and sentiment analysis. Found Trends Inf RetR 2(1–2):1–135CrossRef
Zurück zum Zitat Patra BG, Das D, Das A, Prasath R (2015) Shared task on sentiment analysis in Indian languages (sail) tweets-an overview. In: International conference on mining intelligence and knowledge exploration. Springer, pp 650–655 Patra BG, Das D, Das A, Prasath R (2015) Shared task on sentiment analysis in Indian languages (sail) tweets-an overview. In: International conference on mining intelligence and knowledge exploration. Springer, pp 650–655
Zurück zum Zitat Phani S, IIEST S, Lahiri S, Biswas A (2016) Sentiment analysis of tweets in three Indian languages. In: Proceedings of the 6th workshop on south and southeast Asian natural language processing, vol 1001, pp 93–102 Phani S, IIEST S, Lahiri S, Biswas A (2016) Sentiment analysis of tweets in three Indian languages. In: Proceedings of the 6th workshop on south and southeast Asian natural language processing, vol 1001, pp 93–102
Zurück zum Zitat Prasad SS, Kumar J, Prabhakar DK, Pal S (2015) Sentiment classification: an approach for Indian language tweets using decision tree. In: International conference on mining intelligence and knowledge exploration. Springer, pp 656–663 Prasad SS, Kumar J, Prabhakar DK, Pal S (2015) Sentiment classification: an approach for Indian language tweets using decision tree. In: International conference on mining intelligence and knowledge exploration. Springer, pp 656–663
Zurück zum Zitat Rani S, Kumar P (2017) A sentiment analysis system to improve teaching and learning. Computer 50(5):36–43CrossRef Rani S, Kumar P (2017) A sentiment analysis system to improve teaching and learning. Computer 50(5):36–43CrossRef
Zurück zum Zitat Rehman ZU, Bajwa IS (2016) Lexicon-based sentiment analysis for Urdu language. In: Sixth international conference on innovative computing technology. IEEE, pp 497–501 Rehman ZU, Bajwa IS (2016) Lexicon-based sentiment analysis for Urdu language. In: Sixth international conference on innovative computing technology. IEEE, pp 497–501
Zurück zum Zitat Rohini V, Thomas M, Latha C (2016) Domain based sentiment analysis in regional language-Kannada using machine learning algorithm. In: International conference on recent trends in electronics, information and communication technology. IEEE, pp 503–507 Rohini V, Thomas M, Latha C (2016) Domain based sentiment analysis in regional language-Kannada using machine learning algorithm. In: International conference on recent trends in electronics, information and communication technology. IEEE, pp 503–507
Zurück zum Zitat Sahu S, Behera P, Mohapatra D, Rakesh C (2016a) Information retrieval in web for an Indian language: an Odia language sentimental analysis context. Int J Comput Technol Appl 9(22):249–256 Sahu S, Behera P, Mohapatra D, Rakesh C (2016a) Information retrieval in web for an Indian language: an Odia language sentimental analysis context. Int J Comput Technol Appl 9(22):249–256
Zurück zum Zitat Sahu SK, Behera P, Mohapatra D, Balabantaray RC (2016b) Sentiment analysis for Odia language using supervised classifier: an information retrieval in Indian language initiative. CSI Trans ICT 4(2–4):111–115CrossRef Sahu SK, Behera P, Mohapatra D, Balabantaray RC (2016b) Sentiment analysis for Odia language using supervised classifier: an information retrieval in Indian language initiative. CSI Trans ICT 4(2–4):111–115CrossRef
Zurück zum Zitat Sarkar K, Chakraborty S (2015) A sentiment analysis system for Indian language tweets. In: International conference on mining intelligence and knowledge exploration. Springer, pp 694–702 Sarkar K, Chakraborty S (2015) A sentiment analysis system for Indian language tweets. In: International conference on mining intelligence and knowledge exploration. Springer, pp 694–702
Zurück zum Zitat Se S, Vinayakumar R, Kumar MA, Soman K (2015) Amrita-cen@ sail2015: Sentiment analysis in Indian languages. In: International conference on mining intelligence and knowledge exploration. Springer, pp 703–710 Se S, Vinayakumar R, Kumar MA, Soman K (2015) Amrita-cen@ sail2015: Sentiment analysis in Indian languages. In: International conference on mining intelligence and knowledge exploration. Springer, pp 703–710
Zurück zum Zitat Se S, Vinayakumar R, Kumar MA, Soman K (2016) Predicting the sentimental reviews in tamil movie using machine learning algorithms. Indian J Sci Technol 9(45):1–5CrossRef Se S, Vinayakumar R, Kumar MA, Soman K (2016) Predicting the sentimental reviews in tamil movie using machine learning algorithms. Indian J Sci Technol 9(45):1–5CrossRef
Zurück zum Zitat Seshadri S, Madasamy AK, Padannayil SK (2016) Analyzing sentiment in indian languages micro text using recurrent neural network. IIOAB 7:313–318 Seshadri S, Madasamy AK, Padannayil SK (2016) Analyzing sentiment in indian languages micro text using recurrent neural network. IIOAB 7:313–318
Zurück zum Zitat Sharma P, Moh TS (2016) Prediction of Indian election using sentiment analysis on Hindi twitter. In: International conference on big data. IEEE, pp 1966–1971 Sharma P, Moh TS (2016) Prediction of Indian election using sentiment analysis on Hindi twitter. In: International conference on big data. IEEE, pp 1966–1971
Zurück zum Zitat Sharma R, Bhattacharyya P (2014) A sentiment analyzer for Hindi using Hindi Senti Lexicon. In: 11th International conference on natural language processing, pp 1–6 Sharma R, Bhattacharyya P (2014) A sentiment analyzer for Hindi using Hindi Senti Lexicon. In: 11th International conference on natural language processing, pp 1–6
Zurück zum Zitat Sharma R, Nigam S, Jain R (2014) Polarity detection movie reviews in Hindi language, pp 1–9. arXiv preprint arXiv:1409.3942 Sharma R, Nigam S, Jain R (2014) Polarity detection movie reviews in Hindi language, pp 1–9. arXiv preprint arXiv:​1409.​3942
Zurück zum Zitat Sharma Y, Mangat V, Kaur M (2015) A practical approach to sentiment analysis of Hindi tweets. In: 1st International conference on next generation computing technologies. IEEE, pp 677–680 Sharma Y, Mangat V, Kaur M (2015) A practical approach to sentiment analysis of Hindi tweets. In: 1st International conference on next generation computing technologies. IEEE, pp 677–680
Zurück zum Zitat Sharmista A, Ramaswami M (2016) Tree based opinion mining in Tamil for product recommendations using R. Int J Comput Intell Inf 6(2):108–116 Sharmista A, Ramaswami M (2016) Tree based opinion mining in Tamil for product recommendations using R. Int J Comput Intell Inf 6(2):108–116
Zurück zum Zitat Syed AZ, Aslam M, Martinez-Enriquez AM (2010) Lexicon based sentiment analysis of Urdu text using SentiUnits. In: Mexican international conference on artificial intelligence. Springer, pp 32–43 Syed AZ, Aslam M, Martinez-Enriquez AM (2010) Lexicon based sentiment analysis of Urdu text using SentiUnits. In: Mexican international conference on artificial intelligence. Springer, pp 32–43
Zurück zum Zitat Syed AZ, Aslam M, Martinez-Enriquez AM (2011) Sentiment analysis of Urdu language: handling phrase-level negation. In: Mexican international conference on artificial intelligence. Springer, pp 382–393 Syed AZ, Aslam M, Martinez-Enriquez AM (2011) Sentiment analysis of Urdu language: handling phrase-level negation. In: Mexican international conference on artificial intelligence. Springer, pp 382–393
Zurück zum Zitat Syed AZ, Aslam M, Martinez-Enriquez AM (2014) Associating targets with sentiunits: a step forward in sentiment analysis of Urdu text. Artif Intell Rev 41(4):535–561CrossRef Syed AZ, Aslam M, Martinez-Enriquez AM (2014) Associating targets with sentiunits: a step forward in sentiment analysis of Urdu text. Artif Intell Rev 41(4):535–561CrossRef
Zurück zum Zitat Thapa LBR, Bal BK (2016) Classifying sentiments in Nepali subjective texts. In: 7th International conference on information, intelligence, systems and applications. IEEE, pp 1–6 Thapa LBR, Bal BK (2016) Classifying sentiments in Nepali subjective texts. In: 7th International conference on information, intelligence, systems and applications. IEEE, pp 1–6
Zurück zum Zitat Thulasi P, Usha K (2016) Aspect polarity recognition of movie and product reviews in Malayalam. In: International conference on next generation intelligent systems. IEEE, pp 1–5 Thulasi P, Usha K (2016) Aspect polarity recognition of movie and product reviews in Malayalam. In: International conference on next generation intelligent systems. IEEE, pp 1–5
Zurück zum Zitat Venugopalan M, Gupta D (2015) Sentiment classification for Hindi tweets in a constrained environment augmented using tweet specific features. In: International conference on mining intelligence and knowledge exploration. Springer, pp 664–670 Venugopalan M, Gupta D (2015) Sentiment classification for Hindi tweets in a constrained environment augmented using tweet specific features. In: International conference on mining intelligence and knowledge exploration. Springer, pp 664–670
Metadaten
Titel
A journey of Indian languages over sentiment analysis: a systematic review
verfasst von
Sujata Rani
Parteek Kumar
Publikationsdatum
12.12.2018
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 2/2019
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-018-9670-y

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