Cambria E, Kumar A, Al-Ayyoub M, Howard N. Guest Editorial: explainable artificial intelligence for sentiment analysis. Elsevier; 2021.

Liang B, Su H, Gui L, Cambria E, Xu R. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl-Based Syst. 2022;235:107643.

Mohammad SM, Turney PD. Crowdsourcing a word-emotion association lexicon. Comput Intell. 2013;29(3):436–65.

MathSciNet  Article  Google Scholar 

Mohammad SM. Word affect intensities. arXiv preprint arXiv:1704.08798. 2017.

Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. 2005. p. 347–54.

Stone PJ, Dunphy DC, Smith MS. The general inquirer: a computer approach to content analysis. 1966.

Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. p. 168–77.

Bravo-Marquez F, Khanchandani A, Pfahringer B. Incremental word vectors for time-evolving sentiment lexicon induction. Cogn Comput. 2022;14(1):425–41.

Article  Google Scholar 

Sharma SS, Dutta G. Sentidraw: using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Inf Process Manag. 2021;58(1):102412.

Huang M, Xie H, Rao Y, Feng J, Wang FL. Sentiment strength detection with a context-dependent lexicon-based convolutional neural network. Inform Sci. 2020;520:389–99.

Article  Google Scholar 

Viegas F, Alvim MS, Canuto S, Rosa T, Gonçalves MA, Rocha L. Exploiting semantic relationships for unsupervised expansion of sentiment lexicons. Inf Syst. 2020;94:101606.

Hutto C, Gilbert E. Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8. 2014.

De Bruyne L, Atanasova P, Augenstein I. Joint emotion label space modeling for affect lexica. Comput Speech Lang. 2022;71:101257.

Bandhakavi A, Wiratunga N, Massie S. Emotion-aware polarity lexicons for twitter sentiment analysis. Expert Syst. 2021;38(7):12332.

Yin F, Wang Y, Liu J, Lin L. The construction of sentiment lexicon based on context-dependent part-of-speech chunks for semantic disambiguation. IEEE Access. 2020;8:63359–67.

Article  Google Scholar 

Du M, Li X, Luo L. A training-optimization-based method for constructing domain-specific sentiment lexicon. Complexity. 2021;2021.

Ekman P. An argument for basic emotions. Cognit Emot. 1992;6(3–4):169–200.

Article  Google Scholar 

James W. The emotions. 1922.

Kilgarriff A. Wordnet: an electronic lexical database. JSTOR; 2000.

Mohammad SM, Kiritchenko S. Using hashtags to capture fine emotion categories from tweets. Comput Intell. 2015;31(2):301–26.

MathSciNet  Article  Google Scholar 

Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013.

Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1532–43.

Cilibrasi RL, Vitanyi PM. The Google similarity distance. IEEE Trans Knowl Data Eng. 2007;19(3):370–83.

Article  Google Scholar 

Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R. Indexing by latent semantic analysis. J Am Soc Inf Sci. 1990;41(6):391–407.

Article  Google Scholar 

Strapparava C, Mihalcea R. Semeval-2007 task 14: affective text. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007). 2007. p. 70–4.

Wang W, Chen L, Thirunarayan K, Sheth AP. Harnessing twitter “big data” for automatic emotion identification. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. IEEE; 2012. p. 587–92.

Bandhakavi A, Wiratunga N, Massie S, Deepak P. Emotion-corpus guided lexicons for sentiment analysis on twitter. In: International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer; 2016. p. 71–85.

Aman S, Szpakowicz S. Identifying expressions of emotion in text. In: International Conference on Text, Speech and Dialogue. Springer; 2007. p. 196–205.

Pang B, Lee L. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs/0506075. 2005.

Potts C. On the negativity of negation. In: Semantics and Linguistic Theory, vol. 20. 2010. p. 636–59.

Nakov P, Kozareva Z, Ritter A, Rosenthal S, Stoyanov V, Wilson T. Semeval-2013 task 2: sentiment analysis in twitter. 2013.

Staiano J, Guerini M. Depechemood: a lexicon for emotion analysis from crowd-annotated news. arXiv preprint arXiv:1405.1605. 2014.

Badaro G, Jundi H, Hajj H, El-Hajj W. Emowordnet: automatic expansion of emotion lexicon using English wordnet. In: Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics. 2018. p. 86–93.

Wang L, Xia R. Sentiment lexicon construction with representation learning based on hierarchical sentiment supervision. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. p. 502–10.

Tang D, Wei F, Qin B, Zhou M, Liu T. Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of Coling 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 2014. p. 172–82.

Vo DT, Zhang Y. Don’t count, predict! an automatic approach to learning sentiment lexicons for short text. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2. 2016. p. 219–24.

Mohammad SM, Kiritchenko S, Zhu X. NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242. 2013.

Suttles J, Ide N. Distant supervision for emotion classification with discrete binary values. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer; 2013. p. 121–36.

Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. J Am Soc Inf Sci Technol. 2012;63(1):163–73.

Article  Google Scholar