@conference {875,
	title = {A Comparative Study of Qwen and Gemma Large Language Models for Social Media Slang and Emoji Sentiment Analysis for Business Recommender Systems},
	booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications},
	year = {2026},
	publisher = {IEEE Xplore},
	organization = {IEEE Xplore},
	abstract = {<p>Sentiment analysis of social media text remains a fundamentally challenging task due to the pervasive use of informal slang, abbreviations, deliberate spelling modifications, and emojis. Traditional lexicons and early machine learning models frequently struggle to decode the nuanced semantics of these modern digital expressions, particularly when distinguishing between genuine sentiment and sarcasm. In this paper, we present a comprehensive comparative study investigating the capabilities of two recent 4-billion-parameter large language models (LLMs), Qwen3.5 (4B) and Gemma3 (4B), in robust social media sentiment analysis. The core contribution of our study is the fine-tuning of these models at a comparable scale using 4-bit NormalFloat (NF4) quantization combined with the Unsloth library, establishing a highly efficient and reproducible training pipeline. We critically evaluate both models in zero-shot and fine-tuned settings on a diverse, emoji-heavy dataset characterized by four complex sentiment classes: Positive, Negative, Neutral, and Sarcastic. Our experimental results demonstrate that while both models show highly capable zero-shot generalization (Qwen achieving 75.95\% accuracy and Gemma achieving 73.20\%), they exhibit distinct failure modes surrounding sarcastic interpretations. However, alignment via Low-Rank Adaptation (LoRA) dramatically elevates their performance, allowing both to achieve 100\% accuracy on the held-out test set while maintaining distinctly competitive inference speeds suitable for real-time applications. We also provide an exhaustive architectural analysis of the models{\textquoteright} inference latency, per-class prediction bottlenecks, and training efficiency, highlighting practical considerations and trade-offs for deploying lightweight LLMs in real-world sentiment analysis environments. The strong inference-speed profile of both fine-tuned models makes them directly deployable as opinion-mining modules within business recommender system pipelines, where timely and accurate decoding of customer-expressed sentiment{\textemdash}including sarcasm{\textemdash}translates into richer preference signals and more reliable personalization.</p>
},
	keywords = {Gemma, large language models, LoRA, Quantization, Qwen, Sentiment Analysis, social media, Unsloth},
	author = {Konstantinos I. Roumeliotis and Dimitris Spiliotopoulos and Christos Tryfonopoulos and Dionisis Margaris and Giorgos Mpardis and Costas Vassilakis}
}
@article {817,
	title = {Multichannel Digital Marketing Optimizations through Big Data Analytics in the Tourism and Hospitality Industry},
	journal = {Journal of Theoretical and Applied Electronic Commerce Research},
	volume = {17},
	year = {2022},
	month = {oct},
	pages = {1383{\textendash}1408},
	abstract = {<p>The tourism sector increasingly relies on technology to acquire new clients in a world overflowing with information. So, the main question that needs to be answered is:What digital marketing strategy should be adopted to attract customers and built digital brand name by incorporating websites and social media big data? The authors of this research utilize web analytics and big data to build an innovative methodology in an effort to address this issue. After the data collection, statistical analysis was implemented, followed by a fuzzy cognitive map and an agent-based simulation model in order to illustrate the usage of social media and user experience in multichannel marketing. The findings suggest that, in contrast to the websites of other industries, such as logistics, where customers want to finish their inquiries as quickly as possible and leave the webpage, it is advantageous for tourism websites to keep customers{\textquoteright} attention moreon their website in order to increasevisibility. Additionally, the research further highlights the importance of personalization and user-engagement content to e-WOM, suggesting to tourism businesses to encourage posts made by customers and employees.</p>
},
	keywords = {advertising, big data, brand, digital marketing, e-WOM, hospitality, multichannel, social media, tourism, web analytics},
	doi = {10.3390/jtaer17040070},
	url = {https://www.mdpi.com/0718-1876/17/4/70},
	author = {Damianos P. Sakas and Dimitrios P. Reklitis and Marina C. Terzi and Costas Vassilakis}
}
@conference {713,
	title = {TripMentor Project: scope and challenges},
	booktitle = {Proceedings of the Workshop on Cultural Informatics, co-located with the 14th International Workshop On Semantic And Social Media Adaptation And Personalization (SMAP 2019)},
	year = {2019},
	abstract = {We present Tripmentor, a novel project, that is related to tourism in the region of Attica. The project has as main scope to provide rich media content to tourists through a web and mobile environment in two languages trying to guide them through alternative routes that include places of interest as well as organizations with offers to tourists. The project interconnects people with places and events in an automated manner trying to personalize on each tourist{\textquoteright}s personality. The challenges of the project are mainly technological as through the processes of the project a) automated information about venues must be collected, b) information about events related to venues must be fetched and finally c) data related to system users and mainly their profile should be discovered and created in order to offer a unique experience to each of them. We present the scope of the project as well as the challenges of its implementation.},
	keywords = {cultural related information, geodata, personalization, social media, tourism, tripmentor},
	url = {http://ceur-ws.org/Vol-2412/paper6.pdf},
	author = {Costas Vassilakis and Vassilis Poulopoulos and Manolis Wallace and Angeliki Antoniou and George Lepouras},
	editor = {Angeliki Antoniou and Manolis Wallace}
}
@conference {poulopoulos2018a,
	title = {The Effect of Social Media Trending Topics Related to Cultural Venues{\textquoteright} Content},
	booktitle = {2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)},
	year = {2018},
	month = {sep},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Social media have gained the majority of attention on the Internet having an extreme number of daily visitors worldwide. The amount of information exchanged is vast, while users have become equally producers and consumers of data. Words like {\textquotedblleft}trending{\textquotedblright}, {\textquotedblleft}influencers{\textquotedblright}, {\textquotedblleft}likes{\textquotedblright} and {\textquotedblleft}viral{\textquotedblright} are in the daily agenda of data analyzers, as they are associated with factors that play an important role in the influence of social media content to its audience. These aspects are nowadays strongly taken into account by organizations that want to draw the public{\textquoteright}s attention to the content they deliver, and in this context cultural institutions have already started to take under great consideration not only their presence in social media, but also the exploitation of social media dynamics. In this paper we propose a method that can enable cultural venues to benefit from matches between their own content and ongoing discussions on social media. More specifically we extract trending topics that can be related semantically with the content of a cultural institute and examine how a venue can benefit by exploiting these matches. The proposed algorithm has been developed in the scope of the {\textquotedblleft}CrossCult{\textquotedblright} H2020 project, and has been experimentally tested by analyzing the case of Twitter in Greek language.},
	keywords = {cultural informatics, cultural venues, personalization, social media, trending topics},
	doi = {10.1109/SMAP.2018.8501878},
	author = {Vassilis Poulopoulos and Costas Vassilakis and Manolis Wallace and Angela Antoniou and Giorgos Lepouras}
}
@conference {vacilosEuromed18,
	title = {Personality Analysis of Social Media Influencers as a Tool for Cultural Institutions},
	booktitle = {Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection},
	year = {2018},
	pages = {236{\textendash}247},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Nowadays, more and more cultural venues tend to utilize social media as a main tool for marketing, spreading their messages, engaging public and raising public awareness towards culture. It comes to a point where the massive of content in social media makes it a tedious procedure to contact the appropriate audience, the people that would really be stimulated by cultural information. In this notion, we assume that establishing conversations of high impact can possibly guide the cultural venues to audiences that can benefit more. These conversations usually include the so called influencers, users whose opinion can affect many people on social media; the latter usually referred to as followers. In this research paper we examine the characteristics of the influencers that can affect the procedures of a cultural venue on social media. The research is done within the scope of "CrossCult" EU funded project.},
	keywords = {cultural informatics, influencers, personality traits, social media, user modeling},
	doi = {10.1007/978-3-030-01762-0_20},
	author = {Vassilis Poulopoulos and Costas Vassilakis and Angela Antoniou and Giorgos Lepouras and Manolis Wallace}
}
