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Knowledge and Information Systems

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04.07.2018 | Survey Paper

A survey of sentiment analysis in social media

verfasst von: Lin Yue, Weitong Chen, Xue Li, Wanli Zuo, Minghao Yin

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2019

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Abstract

Sentiments or opinions from social media provide the most up-to-date and inclusive information, due to the proliferation of social media and the low barrier for posting the message. Despite the growing importance of sentiment analysis, this area lacks a concise and systematic arrangement of prior efforts. It is essential to: (1) analyze its progress over the years, (2) provide an overview of the main advances achieved so far, and (3) outline remaining limitations. Several essential aspects, therefore, are addressed within the scope of this survey. On the one hand, this paper focuses on presenting typical methods from three different perspectives (task-oriented, granularity-oriented, methodology-oriented) in the area of sentiment analysis. Specifically, a large quantity of techniques and methods are categorized and compared. On the other hand, different types of data and advanced tools for research are introduced, as well as their limitations. On the basis of these materials, the essential prospects lying ahead for sentiment analysis are identified and discussed.

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http://www.journalism.org/2014/05/22/the-eu-elections-on-twitter/.

http://www.queenslandimage.com/.

Adams W, Iyengar G, Lin CY, Naphade MR, Neti C, Nock HJ, Smith JR (2003) Semantic indexing of multimedia content using visual, audio, and text cues. EURASIP J Adv Signal Process 2:987184CrossRef

Akhtar MS, Gupta D, Ekbal A, Bhattacharyya P (2017) Feature selection and ensemble construction: a two-step method for aspect based sentiment analysis. Knowl Based Syst 125:116–135CrossRef

Alam F, Riccardi G (2014) Predicting personality traits using multimodal information. In: Proceedings of the 2014 ACM multi media on workshop on computational personality recognition, 2014. ACM, pp 15–18

Almatrafi O, Parack S, Chavan B (2015) Application of location-based sentiment analysis using Twitter for identifying trends towards Indian general elections 2014. In: Proceedings of the 9th international conference on ubiquitous information management and communication, 2015. ACM, p 41

Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl Based Syst 108:110–124CrossRef

Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236–246CrossRef

Asghar MZ, Khan A, Ahmad S, Qasim M, Khan IA (2017) Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS ONE 12(2):e0171649CrossRef

Atassi H, Esposito A (2008) A speaker independent approach to the classification of emotional vocal expressions. In: 20th IEEE international conference on tools with artificial intelligence, 2008. ICTAI’08, 2008. IEEE, pp 147–152

Baltrušaitis T, Banda N, Robinson P (2013) Dimensional affect recognition using continuous conditional random fields. In: 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), 2013. IEEE, pp 1–8

10.

Baltrušaitis T, Robinson P, Morency LP (2012) 3D constrained local model for rigid and non-rigid facial tracking. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), 2012. IEEE, pp 2610–2617

11.

Banea C, Mihalcea R, Wiebe J (2014) Sense-level subjectivity in a multilingual setting. Comput Speech Lang 28(1):7–19CrossRef

12.

Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters, 2010. Association for Computational Linguistics, pp 36–44

13.

Benamara F, Chardon B, Mathieu YY, Popescu V (2011) Towards context-based subjectivity analysis. In: IJCNLP, 2011. pp 1180–1188

14.

Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. ACL 2007:440–447

15.

Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25(8):1719–1731CrossRef

16.

Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8CrossRef

17.

Bollen J, Pepe A, Mao H (2009) Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. arXiv preprint arXiv:09111583

18.

Burton K, Java A, Soboroff I (2009) The ICWSM 2009 spinn3r dataset. In: Proceedings of the 3rd annual conference on weblogs and social media (ICWSM 2009), San Jose, CA, 2009

19.

Burton K, Kasch N, Soboroff I (2011) The ICWSM 2011 spinn3r dataset. In: Proceedings of the 5th international conference on weblogs and social media (ICWSM 2011), Barcelona, Spain, 2011

20.

Busso C, Bulut M, Lee CC, Kazemzadeh A, Mower E, Kim S, Chang JN, Lee S, Narayanan SS (2008) IEMOCAP: interactive emotional dyadic motion capture database. Lang Resour Eval 42(4):335CrossRef

21.

Cambria E, Howard N, Hsu J, Hussain A (2013) Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics. In: 2013 IEEE symposium on computational intelligence for human-like intelligence (CIHLI), 2013. IEEE, pp 108–117

22.

Cha M, Pérez J, Haddadi H (2009) Flash floods and ripples: The spread of media content through the blogosphere. In: ICWSM 2009: proceedings of the 3rd AAAI international conference on weblogs and social media, 2009

23.

Chen YC, Cheng JY, Hsu HH (2016) A cluster-based opinion leader discovery in social network. In: 2016 conference on technologies and applications of artificial intelligence (TAAI), 2016. IEEE, pp 78–83

24.

Chmiel A, Sienkiewicz J, Thelwall M, Paltoglou G, Buckley K, Kappas A, Hołyst JA (2011) Collective emotions online and their influence on community life. PLoS ONE 6(7):e22207CrossRef

25.

Chmiel A, Sobkowicz P, Sienkiewicz J, Paltoglou G, Buckley K, Thelwall M, Hołyst JA (2011) Negative emotions boost user activity at BBC forum. Physica A 390(16):2936–2944CrossRef

26.

Choi Y, Breck E, Cardie C (2006) Joint extraction of entities and relations for opinion recognition. In: Proceedings of the 2006 conference on empirical methods in natural language processing, 2006. Association for Computational Linguistics, pp 431–439

27.

Cohen I, Sebe N, Gozman F, Cirelo MC, Huang TS (2003) Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data. In: 2003. Proceedings. 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. IEEE, pp I–I

28.

Corradini A, Mehta M, Bernsen NO, Martin J, Abrilian S (2005) Multimodal input fusion in human-computer interaction. NATO Sci Ser Sub Ser III Comput Syst Sci 198:223

29.

Dahake PP, Shaw K, Malathi P (2016) Speaker dependent speech emotion recognition using MFCC and support vector machine. In: International conference on automatic control and dynamic optimization techniques (ICACDOT), 2016. IEEE, pp 1080–1084

30.

Datcu D, Rothkrantz LJ (2014) Semantic audio-visual data fusion for automatic emotion recognition. Emotion Recogn Pattern Anal Approach 411–435

31.

Deng L, Wiebe J (2016) Recognizing opinion sources based on a new categorization of opinion types. IJCAI 2016:2775–2781

32.

Douglas-Cowie E, Cowie R, Schröder M (2000) A new emotion database: considerations, sources and scope. In: ISCA tutorial and research workshop (ITRW) on speech and emotion, 2000

33.

Douglas-Cowie E, Cowie R, Sneddon I, Cox C, Lowry O, Mcrorie M, Martin JC, Devillers L, Abrilian S, Batliner A (2007) The HUMAINE database: addressing the collection and annotation of naturalistic and induced emotional data. Affect Comput Intell Interact 488–500

34.

Ertugrul AM, Onal I, Acarturk C (2017) Does the strength of sentiment matter? A regression based approach on turkish social media. In: International conference on applications of natural language to information systems, 2017. Springer, pp 149–155

35.

Fattah MA (2015) New term weighting schemes with combination of multiple classifiers for sentiment analysis. Neurocomputing 167:434–442CrossRef

36.

Fu H, Niu Z, Zhang C, Yu H, Ma J, Chen J, Chen Y, Liu J (2016) ASELM: adaptive semi-supervised ELM with application in question subjectivity identification. Neurocomputing 207:599–609CrossRef

37.

Fu X, Liu W, Xu Y, Cui L (2017) Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis. Neurocomputing 241:18–27CrossRef

38.

Fu X, Liu W, Xu Y, Yu C, Wang T (2016) Long short-term memory network over rhetorical structure theory for sentence-level sentiment analysis. Asian Conf Mach Learn 2016:17–32

39.

Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv (CSUR) 49(2):28CrossRef

40.

Glodek M, Reuter S, Schels M, Dietmayer K, Schwenker F (2013) Kalman filter based classifier fusion for affective state recognition. In: International workshop on multiple classifier systems, 2013. Springer, pp 85–94

41.

González-Ibánez R, Muresan S, Wacholder N (2011) Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies: short papers, vol 2, 2011. Association for Computational Linguistics, pp 581–586

42.

Guo H, Zhu H, Guo Z, Zhang X, Su Z (2010) OpinionIt: a text mining system for cross-lingual opinion analysis. In: Proceedings of the 19th ACM international conference on Information and knowledge management, 2010. ACM, pp 1199–1208

43.

Hai Z, Chang K, Kim JJ (2011) Implicit feature identification via co-occurrence association rule mining. In: Computational linguistics and intelligent text processing. Springer, pp 393–404

44.

Han J, Zhang Z, Ringeval F, Schuller B (2017) Prediction-based learning for continuous emotion recognition in speech. In: 42nd IEEE international conference on acoustics, speech, and signal processing, ICASSP 2017, 2017

45.

Hassan T, Bajwa IS, Hassan S (2016) Prediction of terrorist activities by using unsupervised learning techniques. J Appl Emerg Sci 6(2):56–60

46.

Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 2004. ACM, pp 168–177

47.

Hu M, Liu B (2004) Mining opinion features in customer reviews. In: AAAI, 2004, vol 4. pp 755–760

48.

Huang X, Kortelainen J, Zhao G, Li X, Moilanen A, Seppänen T, Pietikäinen M (2016) Multi-modal emotion analysis from facial expressions and electroencephalogram. Comput Vis Image Underst 147:114–124CrossRef

49.

Huynh T, He Y, Rüger S (2015) Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis. In: Advances in information retrieval. Springer, pp 447–452

50.

Iosifidis V, Ntoutsi E (2017) Large scale sentiment learning with limited labels. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017. ACM, pp 1823–1832

51.

Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the 4th ACM international conference on web search and data mining, 2011. ACM, pp 815–824

52.

Kang M, Ahn J, Lee K (2018) Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst Appl 94:218–227CrossRef

53.

Karimi S, Shakery A (2017) A language-model-based approach for subjectivity detection. J Inf Sci 43(3):356–377CrossRef

54.

Karyotis C, Doctor F, Iqbal R, James A, Chang V (2017) A fuzzy computational model of emotion for cloud based sentiment analysis. Inf Sci 433–434:448–463

55.

Katiyar A, Cardie C (2016) Investigating LSTMs for joint extraction of opinion entities and relations. In: ACL (1), 2016

56.

Khan FH, Qamar U, Bashir S (2016) eSAP: a decision support framework for enhanced sentiment analysis and polarity classification. Inf Sci 367:862–873CrossRef

57.

Kim SM, Hovy E (2006) Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of the workshop on sentiment and subjectivity in text, 2006. Association for Computational Linguistics, pp 1–8

58.

Kramer AD (2010) An unobtrusive behavioral model of gross national happiness. In: Proceedings of the SIGCHI conference on human factors in computing systems, 2010. ACM, pp 287–290

59.

Lei X, Qian X, Zhao G (2016) Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed 18(9):1910–1921CrossRef

60.

Li N, Zhai S, Zhang Z, Liu B (2017) Structural correspondence learning for cross-lingual sentiment classification with one-to-many mappings. AAAI 2017:3490–3496

61.

Liao J, Wang S, Li D, Li X (2017) FREERL: fusion relation embedded representation learning framework for aspect extraction. Knowl Based Syst 135:9–17CrossRef

62.

Liu B (2010) Sentiment analysis and subjectivity. Handb Nat Lang Process 2:627–666

63.

Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167CrossRef

64.

Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web, 2005. ACM, pp 342–351

65.

Liu S, Zhu W, Xu N, Li F, Cheng XQ, Liu Y, Wang Y (2013) Co-training and visualizing sentiment evolvement for tweet events. In: Proceedings of the 22nd international conference on World Wide Web, 2013. ACM, pp 105–106

66.

Liu Y, Bi JW, Fan ZP (2017) A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Inf Sci 394:38–52CrossRef

67.

Liu Z, Dong X, Guan Y, Yang J (2013) Reserved self-training: a semi-supervised sentiment classification method for chinese microblogs. In: Proceedings of the 6th international joint conference on natural language processing 2013. pp 455–462

68.

Lu B (2010) Identifying opinion holders and targets with dependency parser in Chinese news texts. In: Proceedings of the NAACL HLT 2010 student research workshop, 2010. Association for Computational Linguistics, pp 46–51

69.

Lu Y, Tsaparas P, Ntoulas A, Polanyi L (2010) Exploiting social context for review quality prediction. In: Proceedings of the 19th international conference on World wide web, 2010. ACM, pp 691–700

70.

Manek AS, Shenoy PD, Mohan MC, Venugopal K (2017) Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World wide web 20(2):135–154CrossRef

71.

Martin O, Kotsia I, Macq B, Pitas I (2006) The enterface’05 audio-visual emotion database. In: Proceedings. 22nd international conference on data engineering workshops, 2006, 2006. IEEE, pp 8–8

72.

McKeown G, Valstar M, Cowie R, Pantic M, Schroder M (2012) The semaine database: annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans Affect Comput 3(1):5–17CrossRef

73.

Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113CrossRef

74.

Mitrović M, Paltoglou G, Tadić B (2011) Quantitative analysis of bloggers’ collective behavior powered by emotions. J Stat Mech Theory Exp 02:P02005

75.

Morency LP, Mihalcea R, Doshi P (2011) Towards multimodal sentiment analysis: Harvesting opinions from the web. In: Proceedings of the 13th international conference on multimodal interfaces, 2011. ACM, pp 169–176

76.

Morency LP, Whitehill J, Movellan J (2008) Generalized adaptive view-based appearance model: integrated framework for monocular head pose estimation. In: FG’08. 8th IEEE international conference on automatic face & gesture recognition, 2008, 2008. IEEE, pp 1–8

77.

Navas E, Hernaez I, Luengo I (2006) An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS. IEEE Trans Audio Speech Lang Process 14(4):1117–1127CrossRef

78.

Nefian AV, Liang L, Pi X, Liu X, Murphy K (2002) Dynamic Bayesian networks for audio-visual speech recognition. EURASIP J Adv Signal Process 11:783042MATHCrossRef

79.

Nickel K, Gehrig T, Stiefelhagen R, McDonough J (2005) A joint particle filter for audio-visual speaker tracking. In: Proceedings of the 7th international conference on multimodal interfaces, 2005. ACM, pp 61–68

80.

Ortigosa A, Martín JM, Carro RM (2014) Sentiment analysis in Facebook and its application to e-learning. Comput Hum Behav 31:527–541CrossRef

81.

Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. LREC 2010:1320–1326

82.

Paltoglou G, Thelwall M (2012) Twitter, MySpace, Digg: unsupervised sentiment analysis in social media. ACM Trans Intell Syst Technol (TIST) 3(4):66

83.

Paltoglou G, Thelwall M, Buckely K (2010) Online textual communications annotated with grades of emotion strength. In: Proceedings of the 3rd international workshop of emotion: corpora for research on emotion and affect, 2010. pp 25–31

84.

Pan SJ, Ni X, Sun JT, Yang Q, Chen Z (2010) Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th international conference on World wide web, 2010. ACM, pp 751–760

85.

Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, 2004. Association for Computational Linguistics, p 271

86.

Pang B, Lee L (2005) Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on Association for Computational Linguistics, 2005. Association for Computational Linguistics, pp 115–124

87.

Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRef

88.

Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRef

89.

Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10, 2002. Association for Computational Linguistics, pp 79-86

90.

Paul D, Li F, Teja MK, Yu X, Frost R (2017) Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says! In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017. ACM, pp 1585–1594

91.

Penalver-Martinez I, Garcia-Sanchez F, Valencia-Garcia R, Rodriguez-Garcia MA, Moreno V, Fraga A, Sanchez-Cervantes JL (2014) Feature-based opinion mining through ontologies. Expert Syst Appl 41(13):5995–6008CrossRef

92.

Pérez-Rosas V, Mihalcea R, Morency LP (2013) Utterance-level multimodal sentiment analysis. ACL 1(2013):973–982

93.

Phu VN, Dat ND, Tran VTN, Chau VTN, Nguyen TA (2017) Fuzzy C-means for english sentiment classification in a distributed system. Appl Intell 46(3):717–738CrossRef

94.

Phu VN, Tran VTN, Chau VTN, Dat ND, Duy KLD (2017) A decision tree using ID3 algorithm for English semantic analysis. Int J Speech Technol 1-21

95.

Polpinij J, Ghose AK (2008) An ontology-based sentiment classification methodology for online consumer reviews. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 01, 2008. IEEE Computer Society, pp 518–524

96.

Popescu AM, Etzioni O (2007) Extracting product features and opinions from reviews. In: Natural language processing and text mining. Springer, pp 9–28

97.

Poria S, Cambria E, Gelbukh A (2015) Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of the 2015 conference on empirical methods in natural language processing, 2015. pp 2539–2544

98.

Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl Based Syst 108:42–49CrossRef

99.

Poria S, Cambria E, Howard N, Huang GB, Hussain A (2016) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174:50–59CrossRef

100.

Poria S, Cambria E, Hussain A, Huang GB (2015) Towards an intelligent framework for multimodal affective data analysis. Neural Netw 63:104–116CrossRef

101.

Poria S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 2016 IEEE 16th international conference on data mining (ICDM), 2016. IEEE, pp 439–448

102.

Potamitis I, Chen H, Tremoulis G (2004) Tracking of multiple moving speakers with multiple microphone arrays. IEEE Trans Speech Audio Process 12(5):520–529CrossRef

103.

Pozzi FA, Maccagnola D, Fersini E, Messina E (2013) Enhance user-level sentiment analysis on microblogs with approval relations. In: Congress of the Italian Association for Artificial Intelligence, 2013. Springer, pp 133–144

104.

Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27CrossRef

105.

Qu L, Ifrim G, Weikum G (2010) The bag-of-opinions method for review rating prediction from sparse text patterns. In: Proceedings of the 23rd international conference on computational linguistics, 2010. Association for Computational Linguistics, pp 913–921

106.

Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46CrossRef

107.

Riaz S, Fatima M, Kamran M, Nisar MW (2017) Opinion mining on large scale data using sentiment analysis and k-means clustering. Cluster Computing:1-16

108.

Ruppenhofer J, Somasundaran S, Wiebe J (2008) Finding the sources and targets of subjective expressions. In: LREC, 2008

109.

Saif H, Fernandez M, He Y, Alani H (2013) Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold

110.

Sarkar C, Bhatia S, Agarwal A, Li J (2014) Feature analysis for computational personality recognition using youtube personality data set. In: Proceedings of the 2014 ACM multi media on workshop on computational personality recognition, 2014. ACM, pp 11–14

111.

Schouten K, Frasincar F (2016) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830CrossRef

112.

Si J, Mukherjee A, Liu B, Li Q, Li H, Deng X (2013) Exploiting topic based twitter sentiment for stock prediction. ACL 2(2013):24–29

113.

Snyder B, Barzilay R (2007) Multiple aspect ranking using the good grief algorithm. HLT-NAACL 2007:300–307

114.

Soleymani M, Asghari-Esfeden S, Fu Y, Pantic M (2016) Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans Affect Comput 7(1):17–28CrossRef

115.

Su F, Markert K (2008) From words to senses: a case study of subjectivity recognition. In: Proceedings of the 22nd international conference on computational linguistics, vol 1, 2008. Association for Computational Linguistics, pp 825–832

116.

Suresh H (2016) An unsupervised fuzzy clustering method for twitter sentiment analysis. In: International conference on computation system and information technology for sustainable solutions (CSITSS), 2016. IEEE, pp 80–85

117.

Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 2011. ACM, pp 1397–1405

118.

Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. EMNLP 2015:1422–1432

119.

Tang D, Wei F, Qin B, Liu T, Zhou M (2014) Coooolll: a deep learning system for twitter sentiment classification. In: SemEval@ COLING, 2014. pp 208–212

120.

Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for Twitter sentiment classification. ACL 1(2014):1555–1565

121.

Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Syst Appl 36(7):10760–10773CrossRef

122.

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

123.

Trigeorgis G, Ringeval F, Brueckner R, Marchi E, Nicolaou MA, Schuller B, Zafeiriou S (2016) Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network. In: Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, 2016. IEEE, pp 5200-5204

124.

Tsur O, Davidov D, Rappoport A (2010) ICWSM-a great catchy name: semi-supervised recognition of sarcastic sentences in online product reviews. In: ICWSM, 2010

125.

Tsytsarau M, Palpanas T (2012) Survey on mining subjective data on the web. Data Min Knowl Disc 24(3):478–514MATHCrossRef

126.

Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, 2002. Association for Computational Linguistics, pp 417–424

127.

Valstar MF, Sánchez-Lozano E, Cohn JF, Jeni LA, Girard JM, Zhang Z, Yin L, Pantic M (2017) FERA 2017-addressing head pose in the third facial expression recognition and analysis challenge. arXiv preprint arXiv:170204174

128.

Vaziripour E, Giraud-Carrier CG, Zappala D (2016) Analyzing the political sentiment of tweets in Farsi. ICWSM 2016:699–702

129.

Velikovich L, Blair-Goldensohn S, Hannan K, McDonald R (2010) The viability of web-derived polarity lexicons. In: Human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, 2010. Association for Computational Linguistics, pp 777–785

130.

Verma R, Davatzikos C, Loughead J, Indersmitten T, Hu R, Kohler C, Gur RE, Gur RC (2005) Quantification of facial expressions using high-dimensional shape transformations. J Neurosci Methods 141(1):61–73CrossRef

131.

Vinodhini G, Chandrasekaran R (2012) Sentiment analysis and opinion mining: a survey. Int J 2(6):282–292

132.

Wan X (2008) Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing, 2008. Association for Computational Linguistics, pp 553–561

133.

Wan X (2009) Co-training for cross-lingual sentiment classification. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: vol 1–vol 1, 2009. Association for Computational Linguistics, pp 235–243

134.

Wang S, Zhu Y, Wu G, Ji Q (2014) Hybrid video emotional tagging using users’ EEG and video content. Multimed Tools Appl 72(2):1257–1283CrossRef

135.

Weninger F, Ringeval F, Marchi E, Schuller BW (2016) Discriminatively trained recurrent neural networks for continuous dimensional emotion recognition from audio. IJCAI 2016:2196–2202

136.

Wiebe J, Riloff E (2005) Creating subjective and objective sentence classifiers from unannotated texts. In: Computational linguistics and intelligent text processing. Springer, pp 486–497

137.

Wiebe J, Wilson T, Cardie C (2005) Annotating expressions of opinions and emotions in language. Lang Resour Eval 39(2–3):165–210CrossRef

138.

Wiegand M, Balahur A, Roth B, Klakow D, Montoyo A (2010)A survey on the role of negation in sentiment analysis. In: Proceedings of the workshop on negation and speculation in natural language processing, 2010. Association for Computational Linguistics, pp 60–68

139.

Wiegand M, Bocionek C, Ruppenhofer J (2016) Opinion holder and target extraction on opinion compounds—a linguistic approach. HLT-NAACL 2016:800–810

140.

Wiegand M, Klakow D (2010) Convolution kernels for opinion holder extraction. In: Human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, 2010. Association for Computational Linguistics, pp 795–803

141.

Wilson TA (2008) Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. ProQuest

142.

Wöllmer M, Kaiser M, Eyben F, Schuller B, Rigoll G (2013) LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework. Image Vis Comput 31(2):153–163CrossRef

143.

Wöllmer M, Weninger F, Knaup T, Schuller B, Sun C, Sagae K, Morency LP (2013) Youtube movie reviews: sentiment analysis in an audio-visual context. IEEE Intell Syst 28(3):46–53CrossRef

144.

Wu F, Zhang J, Yuan Z, Wu S, Huang Y, Yan J (2017) Sentence-level sentiment classification with weak supervision. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, 2017. ACM, pp 973–976

145.

Wu Y, Liu S, Yan K, Liu M, Wu F (2014) Opinionflow: visual analysis of opinion diffusion on social media. IEEE Trans Vis Comput Graph 20(12):1763–1772CrossRef

146.

Xia R, Xu F, Yu J, Qi Y, Cambria E (2016) Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf Process Manag 52(1):36–45CrossRef

147.

Xiang B, Zhou L (2014) Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, vol 2, short papers, 2014. pp 434–439

148.

Yamasaki T, Fukushima Y, Furuta R, Sun L, Aizawa K, Bollegala D (2015) Prediction of user ratings of oral presentations using label relations. In: Proceedings of the 1st international workshop on affect and sentiment in multimedia, 2015. ACM, pp 33–38

149.

Yang L, Liu B, Lin H, Lin Y (2016) Combining local and global information for product feature extraction in opinion documents. Inf Process Lett 116(10):623–627CrossRef

150.

Yu N (2014) Exploring Co-training strategies for opinion detection. J Assoc Inf Sci Technol 65(10):2098–2110CrossRef

151.

Zhao J, Lan M, Zhu T (2014) ECNU: expression-and message-level sentiment orientation classification in twitter using multiple effective features. In: SemEval@ COLING, 2014. pp 259–264

152.

Zhao K, Cong G, Yuan Q, Zhu KQ (2015) SAR: a sentiment-aspect-region model for user preference analysis in geo-tagged reviews. In: 2015 IEEE 31st international conference on data engineering (ICDE), 2015. IEEE, pp 675–686

153.

Zhu J, Wang H, Tsou BK, Zhu M (2009) Multi-aspect opinion polling from textual reviews. In: Proceedings of the 18th ACM conference on information and knowledge management, 2009. ACM, pp 1799–1802

Titel: A survey of sentiment analysis in social media
verfasst von: Lin Yue
Weitong Chen
Xue Li
Wanli Zuo
Minghao Yin
Publikationsdatum: 04.07.2018
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 2/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-018-1236-4

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