Abstract
Two types of techniques have been used in the literature for semantic orientation-based approach for sentiment analysis, viz., (i) corpus based and (ii) dictionary or lexicon or knowledge based. In this chapter, we explore the corpus-based semantic orientation approach for sentiment analysis. Corpus-based semantic orientation approach requires large dataset to detect the polarity of the terms and therefore the sentiment of the text. The main problem with this approach is that it relies on the polarity of the terms that have appeared in the training corpus since polarity is computed for the terms that are in the corpus. This approach has been explored well in the literature due to the simplicity of this approach [29, 120]. This approach initially mines sentiment-bearing terms from the unstructured text and further computes the polarity of the terms. Most of the sentiment-bearing terms are multi-word features unlike bag-of-words, e.g., “good movie,” “nice cinematography,” “nice actors,” etc. Performance of semantic orientation-based approach has been limited in the literature due to inadequate coverage of the multi-word features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums. ACM Trans Inf Syst 26(3):1–34
Abbasi A (2010) Intelligent feature selection for opinion classification. IEEE Intell Syst 25(4):75–79
Agarwal B, Mittal N (2012) Text classification using machine learning methods-a survey. In: Proceedings of the 2nd international conference on soft computing for problem solving (SocPros-2012), vol 236, no 1. Jaipur, India, pp 701–710
Agarwal B, Mittal N (2012) Categorical probability proportion difference (CPPD): a feature selection method for sentiment classification. In: Proceedings of the 2nd workshop on sentiment analysis where AI meets psychology, COLING 2012. Mumbai, India, pp 17–26
Agarwal B, Mittal N (2013) Sentiment classification using rough set based hybrid feature selection. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (WASSA’13), NAACL-HLT. Atlanta, pp 115–119
Agarwal B, Mittal N, Sharma VK (2013) Feature extraction methods for semantic orientation based approaches to sentiment analysis. In: Proceedings of the 10th international conference on natural language processing. Noida, India, pp 225–230
Agarwal B, Mittal N (2013) Optimal feature selection for sentiment analysis. In: Proceedings of the 14th international conference on intelligent text processing and computational linguistics (CICLing 2013), vol 7817, no 1. Samos, Greece, pp 13–24
Agarwal B, Mittal N, Cambria E (2013) Enhancing sentiment classification performance using bi-tagged phrases. In: Proceedings of the 13th IEEE international conference on data mining workshops. Dallas, USA, pp 892–895
Agarwal B, Mittal N (2014) Semantic feature clustering for sentiment analysis of English reviews. IETE J Res Taylor Francis 60(6):414–422
Agarwal B, Mittal N (2014) Prominent feature extraction for review analysis: an empirical study. J Exp Theor Artif Intell. Taylor Francis. doi:10.1080/0952813X.2014.977830
Agarwal B, Mittal N, Bansal P, Garg S (2015) Sentiment analysis using common-sense and context information. Comput Intell Neurosci. Article ID 715730, 9. doi:http://dx.doi.org/10.1155/2015/715730
Agarwal B, Poria S, Mittal N, Gelbukh A, Hussain A (2015) Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn Comput 7(4):487–499
Agarwal B, Sharma VK, Mittal N (2013) Sentiment classification of review documents using phrases patterns. In: Second international symposium on natural language processing (NLP’13). Mysore, India, pp 1577–1580
Agarwal B, Mittal N, Sharma VK (2014) Semantic orientation based approaches for sentiment analysis. In: Issac B, Israr N (eds) Case studies in intelligent computing – achievements and trends. CRC, Taylor & Francis, pp 62–75
Agarwal B, Mittal N (2014) Machine learning approaches for sentiment analysis. In: Bhatnagar V (ed) Data mining and analysis in the engineering field. IGI Global, Hershey, pp 193–208
Aphinyanaphongs Y, Fu LD, Li Z, Peskin ER, Efstathiadis E, Aliferis CF, Statnikov A (2014) A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization. J Assoc Inf Sci Technol 65(10):1964–1987
Bakliwal A, Arora P, Patil A, Verma V (2011) Towards enhanced opinion classification using NLP techniques. In: Proceedings of the 5th international joint conference on natural language processing (IJCNLP). Chiang Mai, Thailand, pp 101–107
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 association for computational linguistics (ACL). Prague, Czech Republic, pp 440–447
Cambria E, Havasi C, Hussain A (2012) SenticNet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of the 25th Florida artificial intelligence research society conference (FLAIRS). Florida, US, pp 202–207
Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21
Cambria E, White B (2014) Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag 9(2):48–57
Cambria E, Fu J, Bisio F, Poria S (2015) AffectiveSpace 2: enabling affective intuition for concept-level sentiment analysis. In: Twenty-ninth AAAI conference on artificial intelligence. Austin Texas, USA, pp 508–514
Cambria E, Poria S, Bisio F, Bajpai R, Chaturvedi I (2015) The CLSA model: a novel framework for concept-level sentiment analysis. In: Computational linguistics and intelligent text processing. Cairo, Egypt, pp 3–22
Cambria E, Poria S, Gelbukh A, Kwok K (2014) Sentic API: a common-sense based API for concept-level sentiment analysis. In: Proceedings of the 4th workshop on making sense of microposts (# Microposts2014), co-located with the 23rd international World Wide Web conference (WWW 2014), CEUR workshop proceedings, vol 1141, Seoul, pp 19–24
Chikersal P, Poria S, Cambria E (2015) SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the international workshop on semantic evaluation. Denver, Colorado, USA, SemEval 2015.
Prerna C, Poria S, Cambria E, Gelbukh A, Siong CE (2015) Modelling public sentiment in Twitter: using linguistic patterns to enhance supervised learning. In: Computational linguistics and intelligent text processing. Springer International Publishing. Switzerland, pp 49–65
Cui H, Mittal V, Datar M (2006) Comparative experiments on sentiment classification for online product reviews. In: Proceedings of the 21st national conference on artificial intelligence. Boston, Massachusetts, pp 1265–1270
Dai L, Chen H, Li X (2011) Improving sentiment classification using feature highlighting and feature bagging. In: Proceedings of the 11th IEEE international conference on data mining workshops (ICDMW). Vancouver, Canada, pp 61–66
Dang Y, Zhang Y, Chen H (2010) A lexicon enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46–53
Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web (WWW). Budapest, Hungary, pp 519–528
Denecke K (2008) Using SentiWordNet for multilingual sentiment analysis. In: Proceedings of the 24th international conference on data engineering workshop (ICDEW 2008). Cancun, Maxico, pp 507–512
Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41(7):3506–3513
Devitt A, Ahmad K (2007) Sentiment polarity identification in financial news: a cohesion-based approach. In: Proceedings of the 45th annual meeting of the association of computational linguistics. Prague, Czech Republic, pp 984–991
Dinu LP, Iuga I (2012) The Naive Bayes classifier in opinion mining: in search of the best feature set. In: Proceedings of the 13th international conference on intelligent text processing and computational linguistics, CICLing, vol 7181, no 1. New Delhi, India, pp 556–567
Duric A, Song F (2011) Feature selection for sentiment analysis based on content and syntax models. In: Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, ACL-HLT. Portland, Oregon, USA, pp 96–103
Esuli A, Sebastiani F (2005) Determining the semantic orientation of terms through gloss analysis. In: Proceedings of the 14th ACM international conference on information and knowledge management (CIKM). Bremen, Germany, pp 617–624
Esuli A, Sebastiani F (2006) SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of 5th conference on language resources and evaluation (LREC). Genoa, Italy, pp 417–422
Fahrni A, Klenner M (2008) Old wine or warm beer: target-specific sentiment analysis of adjectives. In: Proceedings of the AISB 2008 symposium on affective language in human and machine. The Society for the Study of Artificial Intelligence and Simulation of Behaviour Press. Aberdeen, UK, pp 60–63
Fei Z, Liu J, Wu G (2004) Sentiment classification using phrase pattern. In: Proceedings of the fourth international conference on computer and infor-mation technology (CIT’04). Wuhan, China, pp 1147–1152
Ferreira L, Jakob N, Gurevych I (2008) A comparative study of feature extraction algorithms in customer reviews. In: Proceedings of the 2nd IEEE International conference on semantic computing. Santa Clara, USA, pp 144–151
Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3(1):1289–1305
Gamon M (2004) Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th international conference on computational linguistics. Geneva, Switzerland, pp 841–848
Goujon B (2011) Text mining for opinion target detection. In: Proceedings of the European intelligence and security informatics conference (EISIC). Athens, Greece, pp 322–326
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(1):1157–1182
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. J SIGKDD Explor 11(1):10–18
Harlambous Y, Klyuev V (2013) Thematically reinforced explicit semantic analysis. Int J Comput Linguist Appl 4(1):79–94
Hatzivassiloglou V, McKeown KR (1997) Predicting the seman-tic orientation of adjectives. In: Proceedings of the thirty-fifth annual meeting of the Association for Computational Linguistics and the eighth conference of the European chapter of the Association for Computational Linguistics. Madrid, Spain, pp 174–181
Havasi C, Speer R, Alonso J (2007) Conceptnet 3: a flexible, multilingual semantic network for common sense knowledge. In: Proceedings of the international conference on recent advances in natural language processing (RANLP), pp 27–29
Hiroshi K, Tetsuya N, Hideo W (2004) Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th international conference on computational linguistics (COLING). Geneva, Switzerland, pp 494–500
Hoque N, Bhattacharyya DK, Kalita JK (2014) MIFS-ND: a mutual information-based feature selection method. Expert Syst Appl 41(14):6371–6385
Howard N, Cambria E (2013) Intention awareness: improving upon situation awareness in human-centric environments. Hum-centric Comput Inf Sci 3(9):1–17
Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM international conference on knowledge discovery and data mining. Seattle, USA, pp 168–177
Ikonomakis M, Kotsiantis S, Tampakas V (2005) Text classification using machine learning techniques. WSEAS Trans Comput 4(8):966–974
Joshi M, Penstein-Rose C (2009) Generalizing dependency features for opinion mining. In: Proceedings of the joint conference of the 47th annual meeting of the Association for Computational Linguistics (ACL). Singapore, pp 313–316
Kaji N, Kitsuregawa M (2007) Building lexicon for sentiment analysis from massive collection of HTML documents. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). Prague, June 2007, pp 1075–1083
Kamps J, Marx M, Mokken RJ, Rijke MD (2004) Using wordnet to measure semantic orientation of adjectives. In: Proceedings of the 4th international conference on language resources and evaluation (LREC). Lisbon, Portugal, pp 1115–1118
Kang H, Yoo SJ, Han D (2012) Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl 39(5):6000–6010
Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22(2):110–125
Kim SM, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on computational linguistics (COLING). Geneva, Switzerland, pp 1367–1373
Konig AC, Brill E (2006) Reducing human overhead in text categorization. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. Philadelphia, USA, pp 598–603
Kumar A, Sebastian TM (2012) Sentiment analysis: a perspective on its past, present and future. Int J Intell Syst Appl 4(10):1–14
Li S, Zong C, Wang X (2007) Sentiment classification through combining classifiers with multiple feature sets. In: Proceedings of the international conference on natural language processing and knowledge engineering (NLP-KE). Beijing, China, pp 135–140
Lin Y, Zhang J, Wang X, Zhou A (2012) An information theoretic approach to sentiment polarity classification. In: Proceedings of the 2nd joint WICOW/AIRWeb workshop on web quality. Lyon, France, pp 35–40
Lin Y, Wang X, Zhang J, Zhou A (2012) Assembling the optimal sentiment classifiers. In: Proceedings of the 13th international conference on web information systems engineering, vol 7651, no 1. Paphos, Cyprus, pp 271–283
Liu H, Singh P (2004) ConceptNet – a practical commonsense reasoning tool-kit. BT Technol J Arch 22(4):211–226
Liu B (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing, 2nd edn. Chapman & Hall/CRC, Boca Raton, pp 627–666
Liu B (2012) Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. Morgan & Claypool Publishers, San Rafael
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. Portland, Oregon, USA, pp 142–150
Manning CD, Raghvan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge
Marneffe D, Manning CD (2008) The Stanford typed dependencies representation. In: Proceedings of the workshop on cross-framework and cross-domain parser evaluation. Association for Computational Linguistics. Manchester, UK, pp 1–8
Martineau J, Finin T (2009) Delta TFIDF: an improved feature space for sentiment analysis. In: Proceedings of the third AAAI international conference on weblogs and social media, pp 258–261
Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. In: Proceedings of the 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD). Hanoi, Vietnam, pp 301–311
Meena A, Prabhakar TV (2007) Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In: Advances in information retrieval. Lecture notes in computer science, vol 4425, no 1, pp 573–580
Mejova Y, Srinivasan P (2011) Exploring feature definition and selection for sentiment classifiers. In: Proceedings of the fifth international AAAI conference on weblogs and social media. Barcelona, Spain, pp 546–549
Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41
Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633
Mukherjee S, Joshi S (2013) Sentiment aggregation using ConceptNet ontology. In: Proceedings of the 6th international joint conference on natural language processing (IJCNLP). Nagoya, Japan, pp 570–578
Mukras R, Wiratunga N, Lothian R (2008) Selecting bi-tags for sentiment analysis of text. In: Proceedings of the 27th SGAI international conference on innovative techniques and applications of artificial intelligence. Cambridge, UK, pp 181–194
Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the international conference on empirical methods in natural language processing (EMNLP), pp 412–418
Nakagawa T, Inui K, Kurohashi S (2010) Dependency tree-based sentiment classification using CRFs with hidden variables. In: Proceedings of the human language technologies: annual conference of the North American chapter of the Association for Computational Linguistics. Los Angeles, USA, pp 786–794
Ng V, Dasgupta S, Arifin SMN (2006) Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: Proceedings of the COLING/ACL 2006 main conference poster sessions. Sydney, Australia, pp 611–618
Nguyen DQ, Nguyen DQ, Pham SB (2013) A two-stage classifier for sentiment analysis. In: Proceedings of the 6th international joint conference on natural language processing. Nagoya, Japan, pp 897–901
Nguyen DQ, Nguyen DQ, Vu T, Pham SB (2014) Sentiment classification on polarity reviews: an empirical study using rating-based features. In: Proceedings of the 5th workshop on computational approaches to subjectivity, sentiment and social media analysis. Baltimore, pp 128–135
Nicholls C, Song F (2010) Comparison of feature selection methods for sentiment analysis. In: Proceedings of the 23rd Canadian conference on advances in artificial intelligence. LNCS, vol 6085, no 1. Ottawa, Canada, pp 286–289
Ohana B, Tierney B (2009) Sentiment classification of reviews using SentiWordNet. In: Proceedings of the 9th IT & T conference. Dublin, Ireland, pp 1–9
O’keefe T, Koprinska I (2009) Feature selection and weighting methods in sentiment analysis. In: Proceedings of the 14th Australasian document computing symposium, Sydney, pp 67–74
Osajima I, Shimada K, Endo T (2005) Classification of evaluative sentences using sequential patterns. In: Proceedings of the 11th annual meeting of the association for natural language processing. Takamatsu, pp 1–8
Osgood CE, Succi GJ, Tannenbaum PH (1957) The measurement of meaning. University of Illinois Press, Urbana
Pak A, Paroubek P (2011) Text representation using dependency tree sub-graphs for sentiment analysis. In: Proceedings of the 16th international conference DASFAA workshop, vol 6637, no 1. Hong Kong, China, pp 323–332
Paltoglou G, Thelwallm M (2010) A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics. Uppsala, Swedan, pp 1386–1395
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP). Prague, pp 79–86
Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the Association for Computational Linguistics (ACL). Barcelona, pp 271–278
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Foundations and trends in information retrieval, vol 2, no 1–2. Now Publishers, Hanover, pp 1–135
Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238
Poria S, Agarwal B, Gelbukh A, Hussain A, Howard N (2014) Dependency-based semantic parsing for concept-level text analysis. In: Proceedings of the 15th international conference on intelligent text processing and computational linguistics (CICLing), vol 8403, no 1. Kathmandu, Nepal, pp 113–127
Poria S, Cambria E, Winterstein G, Huang G-B (2014) Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl-Based Syst 69:45–63. http://dx.doi.org/10.1016/j.knosys.2014.05.005, ISSN 0950–7051
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), 21–25 Oct 2012, vol 2. Beijing, China, pp 1251–1255
Poria S, Cambria E, Ku L-W, Gui C, Gelbukh A (2014) A rule-based approach to aspect extraction from product reviews. SocialNLP 2014:28
Poria S, Gelbukh A, Cambria E, Das D, Bandyopadhyay S (2012) Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: 2012 IEEE 12th international conference on data mining workshops (ICDMW). Brussels, Belgium, pp 709–716
Poria S, Gelbukh A, Cambria E, Hussain A, Huang G-B (2014) EmoSenticSpace: a novel framework for affective common-sense reasoning. Knowl-Based Syst 69:108–123
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(2):31–38. doi:10.1109/MIS.2013.4
Poria S, Gelbukh A, Agarwal B, Cambria E, Howard N (2013) Common sense knowledge based personality recognition from text. In: Castro F, Gelbukh A, González M (eds) Advances in soft computing and its applications. Springer, Heidelberg, pp 484–496
Poria S, Cambria E, Howard N, Huang G-B, Hussain A (2015) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174:50–59. Part A, Published 22 January 2016. http://dx.doi.org/10.1016/j.neucom.2015.01.095
Prabowo R, Thelwall M (2009) Sentiment analysis: a combined approach. J Informetr 3(2):143–157
Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st international joint conference on artificial intelligence (IJCAI), pp 1199–1204
Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. J Comput Linguist 37(1):9–27
Raychev V, Nakov P (2009) Language-independent sentiment analysis using subjectivity and positional information. In: Proceedings of the international conference recent advances on natural language processing (RANLP). Borovets, Bulgaria, pp 360–364
Read J (2005) Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL student research workshop. Michigan, USA, pp 43–48
Riloff E, Patwardhan S, Wiebe J (2006) Feature subsumption for opinion analysis. In: Proceedings of the conference on empirical methods in natural language processing. Sydney, Australia, pp 440–448
Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. J Bioinform 23(19):2507–2517
Saleh MR, Martin-Valdivia MT, Montejo-Raez A, Urena-Lopez LA (2011) Experiments with SVM to classify opinions in different domains. Expert Syst Appl 38(12):14799–14804
Salvetti F, Lewis S, Reichenbach C (2004) Automatic opinion polarity classification of movie reviews. Colo Res Linguist 17(1):1–15
Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernandez L (2013) Syntactic dependency-based N-grams as classification features. In: Proceedings of the Mexican international conference on artificial intelligence (MICAI), vol 7630, no 1, pp 1–11
Sidorov G (2013) Syntactic dependency based n-grams in rule based automatic English as second language grammar correction. Int J Comput Linguist Appl 4(2):169–188
Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernandez L (2014) Syntactic n-grams as machine learning features for natural language processing. Expert Syst Appl 41(3):853–860
Simeon M, Hilderman R (2008) Categorical proportional difference: a feature selection method for text categorization. In: Proceedings of the 7th Australasian data mining conference. Glenelg, South Australia, pp 201–208
Singhal K, Agarwal B, Mittal N (2015) Modeling Indian general elections: sentiment analysis of political Twitter data. In: Second international conference on information systems design and intelligent applications, Vol 339, pp 469–477
Stone PJ, Hunt EB (1963) A computer approach to content analysis: studies using the general inquirer system. In: Proceedings of the AFIPS. Detroit, Michigan, pp 241–256
Sureka A, Goyal V, Correa D, Mondal A (2010) Generating domain-specific ontology from common-sense semantic network for target specific sentiment analysis. In: Proceedings of the fifth international conference of the Global WordNet Association. Mumbai, India, pp 1–8
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307
Takamura H, Inui T, Okumura M (2005) Extracting semantic orientations of words using spin model. In: Proceedings of the Association for Computational Linguistics (ACL), pp 133–140
Takamura H, Inui T, Okumura M (2006) Latent variable models for semantic orientations of phrases. In: Proceedings of the 11th European chapter of the Association for Computational Linguistics (EACL), pp 201–208
Takamura H, Inui T, Okumura M (2007) Extracting semantic orientations of phrases from dictionary. In: Proceedings of the joint human language technology/North American chapter of the ACL conference (HLT-NAACL), pp 292–299
Tan S, Zhang J (2008) An empirical study of sentiment analysis for chinese documents. Expert Syst Appl 34(4):2622–2629
Tan LKW, Na JC, Theng YL, Chang KY (2011) Sentence-level sentiment polarity classification using a linguistic approach. In: Proceedings of the 13th international conference on Asia-Pacific digital libraries (ICADL). Beijing, China, pp 77–87
Tan LKW, Na JC, Theng YL, Chang KY (2012) Phrase-level sentiment polarity classification using rule based typed dependencies and additional complex phrases consideration. J Comput Sci Technol 27(3):650–666
Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Syst Appl 36(7):10760–10773
Thet TT, Na JC, Khoo CSG, Shakthikumar S (2009) Sentiment analysis of movie reviews on discus-sion boards using a linguistic approach. In: Proceedings of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion. Hong Kong, pp 81–84
Tsutsumi K, Shimada K, Endo T (2007) Movie review classification based on a multiple classifier. In: Proceedings of the annual meetings of the Pacific Asia conference on language, information and computation (PACLIC), pp 481–488
Tsytsarau M, Palpanas T (2012) Survey on mining subjective data on the web. Data Min Knowl Discov 24(3):478–514
Tu Z, Jiang W, Liu Q, Lin S (2012) Dependency forest for sentiment analysis. In: Proceedings of the first CCF conference on natural language processing and Chinese computing. Beijing, China, pp 69–77
Turney P (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics (ACL). Philadelphia, pp 417–424
Turney P, Littman ML (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346
Verma S, Bhattacharyya P (2009) Incorporating semantic knowledge for sentiment analysis. In: Proceedings of the international conference on natural language processing (ICON), Hyderabad, pp 1–6
Wang S, Li D, Song S, Wei Y, Li H (2009) A feature selection method based on Fisher’s discriminant ratio for text sentiment classification. In: Proceedings of the international conference on web information systems and mining (WISM). Shanghai, China, pp 88–97
Wang L, Wan Y (2011) Sentiment classification of documents based on latent semantic analysis. In: Proceedings of the international conference on advanced research on computer education, simulation and modeling (CESM). Wuhan, China, pp 356–361
Wei CP, Chen YM, Yang CS, Yang CY (2010) Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Inf Syst E-Bus Manag 8(2):149–167
Weichselbraun A, Gindl S, Scharl A (2013) Extracting and grounding context-aware sentiment lexicons. IEEE Intell Syst 28(2):39–46
Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM international conference on information and knowledge management. Bremen, Germany, pp 625–631
Wiebe J, Riloff E (2005) Creating subjective and objective sentence classifiers from unannotated texts. In: Proceedings of the 6th international conference on computational linguistics and intelligent text processing (CICLing). Mexico City, Mexico, pp 486–497
Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing (HLT). Vancouver, B.C., Canada, pp 347–354
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington
Xia R, Zong C (2010) Exploring the use of word relation features for sentiment classification. In: Proceedings of the 23rd international conference on computational linguistics (COLING), pp 1336–1344
Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. J Inf Sci 181(6):1138–1152
Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36(3):6527–6535
Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5(1):1205–1224
Zhang C, Zeng D, Li J, Wang FY, Zuo W (2009) Sentiment analysis of Chinese documents: from sentence to document level. J Am Soc Inf Sci Technol 60(12):2474–2487
Zhang L, Liu B (2011) Identifying noun product features that imply opinions. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, vol 2, no 1, pp 575–580
Zhu J, Xu C, Wang HS (2010) Sentiment classification using the theory of ANNs. J China Univ Posts Telecommun 37(1):58–62
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Agarwal, B., Mittal, N. (2016). Semantic Orientation-Based Approach for Sentiment Analysis. In: Prominent Feature Extraction for Sentiment Analysis. Socio-Affective Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-25343-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-25343-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25341-1
Online ISBN: 978-3-319-25343-5
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)