Abstract
Sentiment analysis (SA) is widely used in various applications such as online opinion gathering for policy directives in government, monitoring of customers and staff satisfaction in corporate bodies in politics and security structures for public tension monitoring. Recently, the field met new challenges where new algorithms must contend with highly unstructured sources for sentiment expressions emanating from online social media fora. This study proposes a lexicon-based procedure to implement tweets sentiment analysis with improved algorithms. To deal with sources devoid of syntactic and grammatical structure, the approach incorporates lexicon-based text pre-processing using lexicon features such as tokenisation, parts-of-speech (POS), stop word removal, stemming, normalisation, word frequency and count for context analysis. However, since data in social network sites that primarily express sentiments are in multimodal forms, an analysis of multimedia content is required (i.e. retrieving text from audio, image and video). Hence a novel lexicon-based methodology and framework for multimodal sentiment analysis of text congregated from audio, images, and videos have been proposed. The lexicon-based approach classified the contents as positive, negative or neutral. The experiment results from STS-Gold datasets (recall = 93.2%, precision = 81.9%, F1-score = 87.2%, Accuracy = 92.29%) show the efficacy of the proposed approach as compared with those of other state-of-the-art research studies.
Similar content being viewed by others
Data availability
The datasets generated during and/or analysed during the current study are available in the STS-Gold Dataset – Kaggle repository, https://www.kaggle.com/datasets/divyansh22/stsgold-dataset.
References
Abdulla NA, Ahmed NA, Shehab MA, al-Ayyoub M, al-Kabi MN, al-rifai S (2014) Towards improving the lexicon-based approach for Arabic sentiment analysis. Int J Inf Technol Web Eng (IJITWE) 9(3):55–71. https://doi.org/10.4018/ijitwe.2014070104
Abirami AM, Gayathri V (2017) A survey on sentiment analysis methods and approach. 2016 Eighth International Conference on Advanced Computing (ICoAC). IEEE, https://doi.org/10.1109/ICoAC.2017.7951748
Agarwal A, Toshniwal D (2018) Application of lexicon based approach in sentiment analysis for short tweets. 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, https://doi.org/10.1109/ICACCE.2018.8441696
Ahmad M et al (2017) Hybrid tools and techniques for sentiment analysis: a review. Int J Multidiscip Sci Eng 8(3):29–33 ISSN: 2045-7057
Akter S, Aziz MT (2016) Sentiment analysis on the Facebook group using lexicon based approach. 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE. https://doi.org/10.1109/CEEICT.2016.7873080
Alessia D et al (2015) Approaches, tools and applications for sentiment analysis implementation. Int J Comput Appl 125(3):26–33
Aung KZ, Myo NN (2017) Sentiment analysis of students’ comment using lexicon based approach. 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS). IEEE. https://doi.org/10.1109/ICIS.2017.7959985
Bhoir P, Kolte S (2015) Sentiment analysis of movie reviews using lexicon approach. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE. https://doi.org/10.1109/ICCIC.2015.7435796
Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly Media, Inc.
Boutet A, Kim H, Yoneki E (2013) What’s in twitter, I know what parties are popular and who you are supporting now! Soc Netw Anal Min 3:1379–1391. https://doi.org/10.1007/s13278-013-0120-1
Cheng L-C, Tsai S-L (2019) Deep learning for automated sentiment analysis of social media. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Vancouver, British, Columbia, Canada, pp. 1001–1004. https://doi.org/10.1145/3341161.3344821
Devika MD, Sunitha C, Ganesh A (2016) Sentiment analysis: a comparative study on different approaches. Procedia Comput Sci 87:44–49. https://doi.org/10.1016/j.procs.2016.05.124
Dhaoui C, Webster CM, Tan LP (2017) Social media sentiment analysis: lexicon versus machine learning. J Consum Mark 34:480–488. https://doi.org/10.1108/JCM-03-2017-2141
Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media. Vol. 8. No. 1
Jurek A, Mulvenna MD, Bi Y (2015) Improved lexicon-based sentiment analysis for social media analytics. Secur Inf 4(1):1–13. https://doi.org/10.1186/s13388-015-0024-x
Kaur R, Kautish S (2019) Multimodal sentiment analysis: A survey and comparison. Int J Serv Sci Manag Eng Technol (IJSSMET) 10(2):38–58. https://doi.org/10.4018/978-1-6684-6303-1.ch098
Kumar A, Garg G (2019) Sentiment analysis of multimodal twitter data. Multimed Tools Appl 78(17):24103–24119. https://doi.org/10.1007/s11042-019-7390-1
Loper E, Bird S (2002) NLTK: the natural language toolkit. https://arxiv.org/abs/cs/0205028. Accessed 10 Apr 2021
Maynard D, Dupplaw D, Hare J (2013) Multimodal sentiment analysis of social media. BCS SGAI Workshop on Social Media Analysis. URI: http://eprints.soton.ac.uk/id/eprint/360546. Accessed 10 Apr 2021
Mudinas A, Zhang D, Levene M (2012) Combining lexicon and learning based approaches for concept-level sentiment analysis. Proceedings of the first international workshop on issues of sentiment discovery and opinion mining. https://doi.org/10.1145/2346676.2346681
Nigam N, Yadav D (2018) Lexicon-based approach to sentiment analysis of tweets using R language. International Conference on Advances in Computing and Data Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_16
Palanisamy P, Yadav V, Elchuri H (2013) Serendio: Simple and Practical lexicon based approach to Sentiment Analysis. Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)
Pamungkas EW, Putri DGP (2016) An experimental study of lexicon-based sentiment analysis on Bahasa Indonesia. 2016 6th International Annual Engineering Seminar (InAES). IEEE, https://doi.org/10.1109/INAES.2016.7821901
Poria S, Cambria E, Howard N, Huang G-B, Hussain A (2016) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174:50–59. https://doi.org/10.1016/j.neucom.2015.01.095
Sahmed S, Jaidka K, Skoric MM (2016) Tweets and votes: A four-country comparison of volumetric and sentiment analysis approaches. Tenth International AAAI Conference on Web and Social Media
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307. https://doi.org/10.1162/COLI_a_00049
Taj S, Shaikh BB, Meghji AF (2019) Sentiment analysis of news articles: a lexicon based approach. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE. https://doi.org/10.1109/ICOMET.2019.8673428
Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web. J Am Soc Inf Sci Technol 63(1):163–173. https://doi.org/10.1002/asi.21662
Vijayarani S, Ilamathi MJ, Nithya M (2015) Pre-processing techniques for text mining-an overview. Int J Comput Sci Commun Netw 5(1):7–16
Vu L, Le T (2017) A lexicon-based method for Sentiment Analysis using social network data. Proceedings of the International Conference on Information and Knowledge Engineering (IKE). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
Zhang L et al (2011) Combining lexicon-based and learning-based methods for Twitter sentiment analysis. HP Lab Tech Rep HPL-2011 89:1–8
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares that there are no conflicts of interest regarding the publication of this manuscript.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Thangavel, P., Lourdusamy, R. A lexicon-based approach for sentiment analysis of multimodal content in tweets. Multimed Tools Appl 82, 24203–24226 (2023). https://doi.org/10.1007/s11042-023-14411-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14411-3