Abstract
Sentiment analysis opens door for understanding opinions conveyed in text data. Polarity lexicon acts as heart in sentiment analysis tasks. Polarity lexicon learning is explored using multiple techniques over years. This survey paper discuss polarity lexicon in two aspects. The first part is literature study which depicts from initial techniques of polarity lexicon creation to the very recent ones. The second part reveal facts about available open source polarity lexicon resources. Also, open research problems and future directions are unveiled. This informative survey is very useful for individuals entering in this arena.
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Andreevskaia A, Bergler S (2006). Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses. In: EACL’06: Proceedings of the European Chapter of the Association for Computational Linguistics, vol. 6, pp. 209-16.
Anthony A, Gamon M (2005). Customizing sentiment classifiers to new domains: A case study. In: Proceedings of international conference on recent advances in natural language processing, vol. 1(3.1), Bulgaria, pp. 2-1.
Baccianella S, Esuli A, Sebastiani F (2010). SentiWordNet 3.0: An enhanced lexical resource for Sentiment Analysis and Opinion Mining. In: Proceedings of the seventh conference on international Language Resources and Evaluation, vol. 10, pp. 2200-4.
Blitzer J, Dredze M, Pereira F (2007) Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification In: Proceedings of 45th annual meeting of the Association of Computational Linguistics, Prague, Czech Republic, June 2007, pp. 440–47.
Bradley MM, Lang PJ (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical Report C-1, Center for Research in Psychophysiology, University of Florida.
Cambria E, Olsher D, Rajagopal D (2014). SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twenty-eighth AAAI conference on artificial intelligence, Quebec City 2014, pp. 1515-21.
Das S, Chen M (2001). Yahoo! for Amazon: Extracting market sentiment from stock message boards. In: Proceedings of the Asia pacific finance association annual conference, vol. 35, p. 43.
deAlbornoz JC, Plaza L, Gervas P (2012). SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis. In: LREC, pp. 3562-3567.
Ding, X, Bing L, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the conference on Web search and Web Data Mining, pp. 231-40.
Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011). Temporal Patterns of Happiness and Information in global social network: Hedonometrics and Twitter. PLoS one, 6(12):e26752. doi:10.1371/journal.pone.0026752
Esuli A, Sebastiani F (2005). Determining the semantic orientation of terms through gloss classification. In: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 617-24.
Esuli A, Sebastiani F (2006a). Determining term subjectivity and term orientation for opinion mining. In: Proceedings of conference of the European chapter of the Association for Computational Linguistics.
Esuli A, Sebastiani F (2006b). SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of Language Resources and Evaluation, vol. 6, pp. 417-22.
General Inquirer http://www.wjh.harvard.edu/~inquirer/ Accessed on 2016 Jan 20.
Gong B, Grauman K, Sha F (2013). Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation. In: ICML’13: Proceedings of the 30th international conference on Machine Learning (ICML), Atlanta, pp. 222-30.
Hassan A, Radev D (2010). Identifying text polarity using random walks. In: Proceedings of annual meeting of the Association for Computational Linguistics.
Hatzivassiloglou V, McKeown K (1997). Predicting the semantic orientation of adjectives. In: Proceedings of 8th conference of Association for Computational Linguistics, pp. 174-81.
Hearst MA (1992). Direction-based text interpretation as an information access refinement. Text-based intelligent systems: Current research and practice in information extraction and retrieval, 1:257-74.
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, ACM, pp. 168-77.
Kamps J, Marx MJ, Mokken RJ, Rijke MD (2004). Using Wordnet to measure semantic orientations of adjectives. In: Proceedings.of LREC’2004, pp. 1115-18.
Kang H, Yoo SJ, Han D (2012). Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews, Expert Syst. Appl., 39(5):6000-10.
Kim SM, Hovy E (2004). Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics, pp. 1367.
Kim SM, Hovy E (2006). Identifying and analyzing judgment opinions. In: Proceedings of Human Language Technology Conference of the North American Chapter of the ACL, pp. 200-7.
Kiritchenko, S., Zhu, X., Mohammad, S. (2014). Sentiment Analysis of Short Informal Texts. J. Artificial Intelligence Res. 50:723-62.
Kiritchenko S, Zhu X, Cherry C, Mohammad S (2014). Detecting Aspects and Sentiment in Customer Reviews. In: Proceedings of the 8th international workshop on Semantic Evaluation Exercises, SemEval-2014, Dublin, Ireland, pp. 437-42.
Liu B (2012). Sentiment analysis and opinion mining, Morgan & Claypool Publishers.
Loughran T, McDonald B (2011). When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks. J. of Finance, 66:1, 35-65.
Micro-WnOp, http://www-3.unipv.it/wnop/#Cerini07 Accessed on 2016 May 15.
Morinaga S, Yamanishi K, Tateishi K, Fukushima T (2002). Mining product reputations on the web. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp. 341-49.
Mohammad S, Dunne C, Dorr B (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 599-608.
Mohammad SM, Kiritchenko S, Zhu X (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of seventh international workshop on Semantic evaluation exercises (SemEval-2013), Atlanta, Georgia, USA.
Mohammad SM, Turney PD (2010). Emotions evoked by common words and phrases: using Mechanical Turk to create an emotion lexicon. In: Proceedings of the NAACL-HLT’10 workshop on computational approaches to analysis and generation of emotion in text, California, ACL, pp. 26-34.
Nielsen FÅ (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903. http://arxiv.org/abs/1103.2903 Accessed on 2016 Jan 20.
Ohana B, Tierney B, Delany S (2011). Domain independent sentiment classification with many lexicons. In: WAINA’11: Advanced information networking and applications. IEEE workshops of international conference, pp. 632-37.
Pang B, Lee L, Vaithyanathan S (2002). Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of conference on empirical methods in natural language processing, vol. 10, pp. 79-86.
Peng W, Park DH (2011). Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. In: Proceedings of fifth international AAAI conference on weblogs and social media, pp. 273-80.
Qiu G, Liu B, Bu J, Chen C (2009). Expanding domain sentiment lexicon through double propagation. In: Proceedings of international joint conference on Artificial Intelligence, vol. 9, pp. 1199-04.
Rao D, Ravichandran D (2009). Semi-supervised polarity lexicon induction. In: Proceedings of the 12th conference of the European chapter of the ACL, pp. 675-82.
Rui H, Liu Y, Whinston A (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Syst. 55(4):863-70.
Sanagar S, Gupta D (2015). Adaptation of multi-domain corpus learned seeds and polarity lexicon for sentiment analysis. In: Proceedings of international conference on computing and network communications, pp.50-58.
Strapparava C, Valitutti A (2004). WordNet-Affect: an affective extension of WordNet. In: LREC’04: Proceedings of 4th international conference on Language Resources and Evaluation, Lisbon, pp. 1083-86.
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011). Lexicon-based methods for sentiment analysis. Computational linguistics 37(2):267-307.
Turney PD (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of 40th meeting of the Association for Computational Linguistics, pp. 417–24.
Valitutti A, Strapparava C, Stock O (2004). Developing affective lexical resources. Psychology J. 2(1): 61-83.
Venugopalan M, Gupta D (2015). An enhanced polarity lexicon by learning-based method using related domain knowledge. Int. J. Information Processing & Management 6(2):61.
Vishnu KS, Apoorva T, Gupta D (2014). Learning domain-specific and domain independent opinion oriented lexicons using multiple domain knowledge In: Proceedings of 7th IEEE international conference on Contemporary Computing, pp. 318-23.
Warriner AB, Kuperman V, Brysbaert M (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior research methods, 5(4), pp. 1191-207.
Wiebe JM (1990). Identifying subjective characters in narrative. In: Proceedings of international conference on computational linguistics, vol. 2, pp. 401-6.
Wiebe J (2000). Learning subjective adjectives from corpora. In: Proceedings of national conference on artificial intelligence, pp. 735-40.
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, ACL, pp. 347-54.
Xia R, Zong C, Hu X, Cambria E (2013). Feature ensemble plus sample selection: domain adaptation for sentiment classification. Intel. Syst. 28(3):10-8.
Zhang Z, Singh PM (2014). Renew: A semi-supervised framework for generating domain specific lexicons and sentiment analysis In: Proceedings of the Association for Computational Linguistics, pp. 542–51.
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Sanagar, S., Gupta, D. (2016). Roadmap for Polarity Lexicon Learning and Resources: A Survey. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_52
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