Skip Abstract Section
Abstract
Many problems related to natural language processing are solved by neural networks and big data. Researchers have previously focused on single-task supervised goals with limited data management to train slogan classification. A multi-task learning framework is used to learn jointly across several tasks related to generating multi-class slogan types. This study proposes a multi-task model named slogan generative adversarial network systems (Slo-GAN) to enhance coherence and diversity in slogan generation, utilizing generative adversarial networks and recurrent neural networks (RNN). Slo-GAN generates a new text slogan-type corpus, and the training generalization process is improved. We explored active learning (AL) and meta-learning (ML) for dataset labeling efficiency. AL reduced annotations by 10% compared to ML but still needed about 70% of the full dataset for baseline performance. The whole framework of Slo-GAN is supervised and trained together on all of these tasks. The text with the higher reporting score level is filtered by Slo-GAN, and a classification accuracy of 87.2% is achieved. We leveraged relevant datasets to perform a cross-domain experiment, reinforcing our assertions regarding both the distinctiveness of our dataset and the challenges of adapting bilingual dialects to one another.
- Somayeh Abdi and Abdollah Irandoust. 2013. The importance of advertising slogans and their proper designing in brand equity. International journal of Organizational leadership 2, 2 (2013), 62–69.Google Scholar
Cross Ref
- Pir Noman Ahmad, Yuanchao Liu, Gauhar Ali, Mudasir Ahmad Wani, and Mohammed ElAffendi. 2023. Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data. Mathematics 11, 12 (2023), 2668. https://doi.org/10.3390/math11122668Google ScholarCross Ref
- Pir Noman Ahmad, Yuanchao Liu, Khalid Khan, Tao Jiang, and Umama Burhan. 2023. BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers. Sensors 23, 23 (2023), 9355.Google ScholarCross Ref
- Abdullah Saleh Alqahtani, P. Saravanan, M. Maheswari, and Sami Alshmrany. 2022. An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval. J Supercomput 78, 6 (2022), 8625–8643. https://doi.org/10.1007/s11227-021-04171-yGoogle ScholarDigital Library
- Shizhe Diao, Xinwei Shen, Kashun Shum, Yan Song, and Tong Zhang. 2021. TILGAN: Transformer-based implicit latent GAN for diverse and coherent text generation. In Findings of the Association for Computational Linguistics: ACL-IJCNLP, 4844–4858 (2021).Google Scholar
- Mostaq Ahmed, Partha Chakraborty, and Tanupriya Choudhury. 2022. Bangla Document Categorization Using Deep RNN Model with Attention Mechanism. In Cyber Intelligence and Information Retrieval. Springer, 137–147.Google Scholar
- Huma Akhtar and Hamna Khalid. 2019. THE USE OF ENGLISH WORDS IN URDU MAGAZINE PRINT ADVERTISEMENTS: AN ANALYSIS. Bahria University Journal of Humanities & Social Sciences 2, 1 (2019), 14–14.Google Scholar
- Khalid Alnajjar, Hadaytullah Kundi, and Hannu Toivonen. 2018. “ Talent, Skill and Support.”: A Method for Automatic Creation of Slogans. In Proceedings of the Ninth International Conference on Computational Creativity, Salamanca, Spain, June 25-29, 2018, Association for Computational Creativity.Google Scholar
- Khalid Alnajjar and Hannu Toivonen. 2021. Computational generation of slogans. Natural Language Engineering 27, 5 (2021), 575–607.Google ScholarCross Ref
- Pir Noman Ahmad, Adnan Muhammad Shah, and KangYoon Lee. 2023. Propaganda Detection in Public Covid-19 Discussion on Social Media. (2023).Google Scholar
- Margaret M. Bradley and Peter J. Lang. 1999. Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical report C-1, the center for research in psychophysiology.Google Scholar
- Xavier A. Carrasco, Ashraf Elnagar, and Mohammed Lataifeh. 2021. A Generative Adversarial Network for Data Augmentation: The Case of Arabic Regional Dialects. Procedia Computer Science 189, (2021), 92–99.Google Scholar
- Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xu. 2022. Contrastnet: A contrastive learning framework for few-shot text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, 2022. 10492–10500.Google ScholarCross Ref
- WenShin Chen and Rudy Hirschheim. 2004. A paradigmatic and methodological examination of information systems research from 1991 to 2001. Information systems journal 14, 3 (2004), 197–235.Google Scholar
- Yizhou Chen, Xu-Hua Yang, Zihan Wei, Ali Asghar Heidari, Nenggan Zheng, Zhicheng Li, Huiling Chen, Haigen Hu, Qianwei Zhou, and Qiu Guan. 2022. Generative adversarial networks in medical image augmentation: a review. Computers in Biology and Medicine (2022), 105382.Google Scholar
- Yu-Fu Chen and Szu-Hao Huang. 2021. Sentiment-influenced trading system based on multimodal deep reinforcement learning. Applied Soft Computing 112, (2021), 107788.Google Scholar
- Kyunghyun Cho, Aaron Courville, and Yoshua Bengio. 2015. Describing multimedia content using attention-based encoder-decoder networks. IEEE Transactions on Multimedia 17, 11 (2015), 1875–1886.Google ScholarDigital Library
- Francesca MM Citron, Brendan S. Weekes, and Evelyn C. Ferstl. 2014. How are affective word ratings related to lexicosemantic properties? Evidence from the Sussex Affective Word List. Applied Psycholinguistics 35, 2 (2014), 313–331.Google ScholarCross Ref
- Giovanni Da San Martino, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, November 2019, Hong Kong, China. Association for Computational Linguistics, Hong Kong, China, 162–170.. https://doi.org/10.18653/v1/D19-5024Google ScholarCross Ref
- Mayukh Dass, Chiranjeev Kohli, and Manaswini Acharya. 2023. An Investigation Into Slogan Design On Creating Slogan–Brand Alignment: Message Clarity and Creativity Enhance While Jingles and Rhymes Weaken Alignment. Journal of Advertising Research 63, 1 (2023), 43–60.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), June 2019, Minneapolis, Minnesota. Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186.. https://doi.org/10.18653/v1/N19-1423Google ScholarCross Ref
- Pir Noman Ahmad, Adnan Muhammad Shah, and KangYoon Lee. 2023. A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain. In Healthcare, 2023. MDPI, 1268.Google Scholar
- Tom Dobber, Nadia Metoui, Damian Trilling, Natali Helberger, and Claes de Vreese. 2021. Do (microtargeted) deepfakes have real effects on political attitudes? The International Journal of Press/Politics 26, 1 (2021), 69–91.Google Scholar
- Mateusz Dolata, Stefan Feuerriegel, and Gerhard Schwabe. 2022. A sociotechnical view of algorithmic fairness. Information Systems Journal 32, 4 (2022), 754–818.Google ScholarCross Ref
- Grahame R. Dowling and Boris Kabanoff. 1996. Computer-aided content analysis: what do 240 advertising slogans have in common? Marketing Letters 7, 1 (1996), 63–75.Google ScholarCross Ref
- [26] Tatjana Dubovičienė and Pavel Skorupa. 2014. The Analysis of some Stylistic Features of English Advertising Slogans. Man & the Word/Zmogus ir zodis 16, 3 (2014).Google Scholar
- Nima Ebadi, Mohsen Jozani, Kim-Kwang Raymond Choo, and Paul Rad. 2021. A memory network information retrieval model for identification of news misinformation. IEEE Transactions on Big Data (2021).Google Scholar
- Ashraf Elnagar, Ridhwan Al-Debsi, and Omar Einea. 2020. Arabic text classification using deep learning models. Information Processing & Management 57, 1 (2020), 102121.Google ScholarDigital Library
- Mauajama Firdaus, Naveen Thangavelu, Asif Ekbal, and Pushpak Bhattacharyya. 2022. I enjoy writing and playing, do you: A Personalized and Emotion Grounded Dialogue Agent using Generative Adversarial Network. IEEE Transactions on Affective Computing, 14, 3 (2022), 2127-2138.Google ScholarDigital Library
- Nuria Galí, Raquel Camprubí, and José A. Donaire. 2017. Analysing tourism slogans in top tourism destinations. Journal of Destination Marketing & Management 6, 3 (2017), 243–251.Google ScholarCross Ref
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27, (2014).Google Scholar
- Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5–6 (2005), 602–610.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- Lindsay Pérez Huber. 2016. Make America great again: Donald Trump, racist nativism and the virulent adherence to white supremecy amid US demographic change. Charleston L. Rev. 10, (2016), 215.Google Scholar
- Kango Iwama and Yoshinobu Kano. 2018. Japanese advertising slogan generator using case frame and word vector. In Proceedings of the 11th International Conference on Natural Language Generation, 2018. 197–198.Google ScholarCross Ref
- Soo-Yeon Jeong and Young-Kuk Kim. 2021. Deep learning-based context-aware recommender system considering contextual features. Applied Sciences 12, 1 (2021), 45.Google ScholarCross Ref
- Ziyun Jiao and Fuji Ren. 2021. WRGAN: Improvement of RelGAN with wasserstein loss for text generation. Electronics 10, 3 (2021), 275.Google ScholarCross Ref
- Boris Katz, Igor Kaplansky, Rebecca Schulman, and Ali Ibrahim. Information Retrieval using Patterns and Relations. MIT Artificial Intelligence Laboratory Research Abstracts, September 2000.Google Scholar
- Qunsheng Ke and Weiwei Wang. 2013. The Adjective Frequency in Advertising English Slogans. Theory & Practice in Language Studies 3, 2 (2013).Google Scholar
- Istvan Kecskes. 2006. On my mind: Thoughts about salience, context and figurative language from a second language perspective. Second Language Research 22, 2 (2006), 219–237.Google ScholarCross Ref
- Md Khan, Akib Zabed, Saif Mahmud Parvez, Md Rahman, and Md Musfique Anwar. 2021. Efficient Advertisement Slogan Detection and Classification Using a Hierarchical BERT and BiLSTM-BERT Ensemble Model. In International Conference on Intelligent Computing & Optimization, 2021. Springer, 964–975.Google ScholarCross Ref
- Gunhee Kim and Eric P. Xing. 2014. Reconstructing storyline graphs for image recommendation from web community photos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. 3882–3889.Google Scholar
- Chiranjeev Kohli, Lance Leuthesser, and Rajneesh Suri. 2007. Got slogan? Guidelines for creating effective slogans. Business horizons 50, 5 (2007), 415–422.Google Scholar
- Yingying Liu, Peipei Li, and Xuegang Hu. 2022. Combining context-relevant features with multi-stage attention network for short text classification. Computer Speech & Language 71, (2022), 101268.Google ScholarDigital Library
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).Google Scholar
- Yong Liu and Xiangnan Gou. 2021. A Text Classification Method Based on Graph Attention Networks. In 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), 2021. IEEE, 35–39.Google ScholarCross Ref
- Gözde Özbal, Daniele Pighin, and Carlo Strapparava. 2013. Brainsup: Brainstorming support for creative sentence generation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2013. 1446–1455.Google Scholar
- Steve Pan. 2019. Tourism slogans–Towards a conceptual framework. Tourism Management 72, (2019), 180–191.Google Scholar
- Steve Pan, Carla Santos, and Seongseop Kim. 2017. Promoting tourism, projecting power: The role of television commercials. Journal of Travel & Tourism Marketing 34, 2 (2017), 192–208.Google ScholarCross Ref
- Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, and Yong Rui. 2016. Jointly modeling embedding and translation to bridge video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. 4594–4602.Google ScholarCross Ref
- Xiao Pu, Lin Yuan, Jiaxu Leng, Tao Wu, and Xinbo Gao. 2023. Lexical knowledge enhanced text matching via distilled word sense disambiguation. Knowledge-Based Systems (2023), 110282.Google Scholar
- Israr Qureshi, Shan L. Pan, and Yingqin Zheng. 2021. Digital social innovation: An overview and research framework. Information Systems Journal 31, 5 (2021), 647–671.Google ScholarCross Ref
- Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015).Google Scholar
- Omar Sharif and Mohammed Moshiul Hoque. 2019. Automatic detection of suspicious Bangla text using logistic regression. In International Conference on Intelligent Computing & Optimization, 2019. Springer, 581–590.Google Scholar
- Saeid Sheikhi. 2021. An effective fake news detection method using WOA-xgbTree algorithm and content-based features. Applied Soft Computing 109, (2021), 107559.Google Scholar
- Gunnar A. Sigurdsson, Xinlei Chen, and Abhinav Gupta. 2016. Learning visual storylines with skipping recurrent neural networks. In European Conference on Computer Vision, 2016. Springer, 71–88.Google ScholarCross Ref
- Angus Stevenson. 2010. Oxford dictionary of English. Oxford University Press, USA.Google Scholar
- Carlo Strapparava, Alessandro Valitutti, and Oliviero Stock. 2007. Automatizing two creative functions for advertising. In Proceedings of the 4th International Joint Workshop on Computational Creativity, 2007. 99–108.Google Scholar
- Jing Su, Qingyun Dai, Frank Guerin, and Mian Zhou. 2021. BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. Computer Speech & Language 67, (2021), 101169.Google ScholarCross Ref
- Ilya Sutskever, James Martens, and Geoffrey E. Hinton. 2011. Generating text with recurrent neural networks. In ICML, 2011.Google Scholar
- Yoad Tewel, Yoav Shalev, Idan Schwartz, and Lior Wolf. 2022. ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 17918–17928.Google ScholarCross Ref
- Andros Tjandra, Sakriani Sakti, Ruli Manurung, Mirna Adriani, and Satoshi Nakamura. 2016. Gated recurrent neural tensor network. In 2016 International Joint Conference on Neural Networks (IJCNN), 2016. IEEE, 448–455.Google ScholarCross Ref
- Polona Tomašic, M. Znidaršic, and Gregor Papa. 2014. Implementation of a slogan generator. In Proceedings of 5th International Conference on Computational Creativity, Ljubljana, Slovenia, 2014. 340–343.Google Scholar
- Polyxeni Vassilakopoulou and Eli Hustad. 2023. Bridging digital divides: a literature review and research agenda for information systems research. Information Systems Frontiers 25, 3 (2023), 955–969.Google ScholarDigital Library
- Tony Veale and Guofu Li. 2013. Creating similarity: Lateral thinking for vertical similarity judgments. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2013. 660–670.Google Scholar
- Subhashini Venugopalan, Marcus Rohrbach, Jeffrey Donahue, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2015. Sequence to sequence-video to text. In Proceedings of the IEEE international conference on computer vision, 2015. 4534–4542.Google ScholarDigital Library
- Hiroaki Yamane and Masafumi Hagiwara. 2015. Advertising slogan generation system reflecting user preference on the web. In Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015), 2015. IEEE, 358–364.Google ScholarCross Ref
- Rui Yang, Pan Zhang, and YongGe Niu. 2018. The effect of tourism slogan message appeals on attitude toward slogan and travel intention: based on the perspective of mental imagery processing theory. Tourism Tribune 33, 6 (2018), 73–86.Google Scholar
- Ming Yuan and Jiangtao Ren. 2021. Numerical Feature Transformation-based Sequence Generation Model for Multi-disease Diagnosis. International Journal of Pattern Recognition and Artificial Intelligence 35, 10 (2021), 2159034.Google ScholarCross Ref
- Roman Zeiss, Anne Ixmeier, Jan Recker, and Johann Kranz. 2021. Mobilising information systems scholarship for a circular economy: Review, synthesis, and directions for future research. Information Systems Journal 31, 1 (2021), 148–183.Google ScholarCross Ref
- Yuqi Zhang, Bin Guo, Yasan Ding, Jiaqi Liu, Chen Qiu, Sicong Liu, and Zhiwen Yu. 2022. Investigation of the determinants for misinformation correction effectiveness on social media during COVID-19 pandemic. Information Processing & Management 59, 3 (2022), 102935.Google ScholarDigital Library
- Ahmad Zuhdi, Cecep Suryana, Rodon Pedrason, Satryo Sasono, and Ahmad Musabiq Habibie. 2023. Digital Campaign: Character Branding and Framing towards the 2024 Presidential Election. Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan 17, 1 (2023), 195–208.Google Scholar
Index Terms
- Enhancing Coherence and Diversity in Multi-Class Slogan Generation Systems
Index terms have been assigned to the content through auto-classification.
Recommendations
Addressing class-imbalance in multi-label learning via two-stage multi-label hypernetwork
Multi-label learning is concerned with learning from data examples that are represented by a single feature vector while associated with multiple labels simultaneously. Existing multi-label learning approaches mainly focus on exploiting label ...
Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer VisionIn graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label ...
Semi-supervised boosting for multi-class classification
ECMLPKDD'08: Proceedings of the 2008th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part IIMost semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit ...
Comments