Abstract
User database of the internet is expanding at a swift rate with the dramatic growth of social media. These include information as well as personal opinions about products, ideas, news, politics, etc. These online opinions and reviews act as a word-to-mouth medium for enhancing or diminishing the popularity of a product, item or concept. Thus, automated analysis of the tone of online opinions helps customers and business personnel significantly to take decisions and develop strategies efficiently. This task, known as sentiment analysis, is an area of active research that relies heavily on the text processing methodology called word embedding. Word embedding is a process of representing text into numeric format, to enable mathematical operations on them. The present study proposes a method of enhancing the performance of word embedding approaches, by integrating sentiment-based information, to render them more suitable for sentiment analysis. Sentiment-based information is incorporated through self-organizing map, where similarity is calculated based on the scores of sentiment-based words. The similarity is further tuned using particle swarm optimization method. Experimentally, performance of the proposed method is justified for sentiment analysis task using various classifiers. Different performance measurement indexes are used to validate the superiority of the proposed method compared to existing approaches.
Similar content being viewed by others
References
Aydoğan E, Akcayol MA (2016) A comprehensive survey for sentiment analysis tasks using machine learning techniques. In: 2016 International symposium on INnovations in intelligent systems and applications, INISTA, IEEE, pp 1–7
Bradley MM, Lang PJ (1999) Affective norms for english words (anew): Instruction manual and affective ratings. Tech. rep., Technical report C-1, the center for research in psychophysiology
Çano E, Morisio M (2019) Word embeddings for sentiment analysis: a comprehensive empirical survey. arXiv:190200753
Caschera MC, Ferri F, Grifoni P (2016) Sentiment analysis from textual to multimodal features in digital environments. In: Proceedings of the 8th International Conference on Management of Digital EcoSystems, pp 137–144
Chaturvedi I, Cambria E, Welsch RE, Herrera F (2018) Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Information Fusion 44:65–77
Code G (2013) [dataset]. https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing
Dragoni M, Petrucci G (2017) A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans Affect Comput 8(4):457–470
D’Urso P, De Giovanni L, Massari R (2020) Smoothed self-organizing map for robust clustering. Inf Sci 512:381–401
Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Citeseer, vol 4, pp 1942–1948
Fu P, Lin Z, Yuan F, Wang W, Meng D (2018) Learning sentiment-specific word embedding via global sentiment representation. In: Thirty-second AAAI conference on artificial intelligence
Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Procedia Computer Science 17:26–32
Hussein DMEDM (2018) A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences 30(4):330–338
Ju S, Li S, Su Y, Zhou G, Hong Y, Li X (2012) Dual word and document seed selection for semi-supervised sentiment classification. In: Proceedings of the 21st ACM international conference on Information and knowledge management, ACM, pp 2295–2298
Kaur A, Gupta V (2013) A survey on sentiment analysis and opinion mining techniques. Journal of Emerging Technologies in Web Intelligence 5 (4):367–371
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1):59–69
Kohonen T (1990) The self-organizing map. Proc IEEE 78 (9):1464–1480
Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1):1–167
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-volume 1, Association for Computational Linguistics, pp 142–150
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:13013781
Ortigosa-Hernández J, JD Rodríguez, Alzate L, Lucania M, Inza I, Lozano JA (2012) Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92:98–115
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, Association for Computational Linguistics, pp 115–124
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-Volume 10, Association for Computational Linguistics, pp 79–86
Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543
Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. https://nlp.stanford.edu/projects/glove/
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intelligence 1(1):33–57
Rezaeinia SM, Ghodsi A, Rahmani R (2017) Improving the accuracy of pre-trained word embeddings for sentiment analysis. arXiv:171108609
Rudkowsky E, Haselmayer M, Wastian M, Jenny M, Emrich Š, Sedlmair M (2018) More than bags of words:, Sentiment analysis with word embeddings. Communication Methods and Measures 12(2-3):140–157
Sagnika S, Pattanaik A, Mishra BSP, Meher SK (2020) A review on multi-lingual sentiment analysis by machine learning methods. J Eng Sci Technol Rev 13(2):154–166
Sarwan NS (2017) Intuitive understanding of word embeddings: From count vectors to word2vec. https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/
Shaikhha H (2017) Github- hammadshaikhha/math-of-machine-learning-course-by-siraj. https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj
Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 1555–1565
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, Association for Computational Linguistics, pp 417–424
Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408
Yang HC, Lee CH, Wu CY (2018) Sentiment discovery of social messages using self-organizing maps. Cognitive Computation 10(6):1152–1166
Yang X, Macdonald C, Ounis I (2018) Using word embeddings in twitter election classification. Information Retrieval Journal 21(2-3):183–207
Yu LC, Wang J, Lai KR, Zhang X (2017) Refining word embeddings for sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 534–539
Zhang Z, Lan M (2015) Learning sentiment-inherent word embedding for word-level and sentence-level sentiment analysis. In: 2015 International Conference on Asian Language Processing (IALP), IEEE, pp 94–97
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sagnika, S., Mishra, B.S.P. & Meher, S.K. Improved method of word embedding for efficient analysis of human sentiments. Multimed Tools Appl 79, 32389–32413 (2020). https://doi.org/10.1007/s11042-020-09632-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09632-9