| En/De/Fr/It- (first 9 out of 22 sentences)
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Srpski - (prvih 9 od 22 rečenica)
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| n1 | Maram Alharbi1,2, | n1 | Maram Alharbi1,2, |
| n2 | Lancaster University, Jazan University | n2 | Lancaster University, Jazan University |
| n3 | m.i.alharbi@lancaster.ac.uk | n3 | m.i.alharbi@lancaster.ac.uk |
| n4 | Ruslan Mitkov | n4 | Ruslan Mitkov |
| n5 | Lancaster University and University of Alicante | n5 | Lancaster University and University of Alicante |
| n6 | r.mitkov@lancaster.ac.uk | n6 | r.mitkov@lancaster.ac.uk |
| n7 | Comparing Rule-based and Deep Learning Approaches for Understanding Sentiments | n7 | Poređenje pristupa zasnovanog na pravilima i dubokog učenja za razumevanje osećanja |
| n8 | In the rapidly evolving domain of sentiment analysis within the hospitality sector, accurately understanding customer sentiment from hotel reviews is increasingly crucial for market intelligence and improving service quality. | n8 | U domenu analize zadovoljstva ugostiteljskim uslugama koja se brzo razvija, tačno razumevanje osećanja i mišljenja korisnika iz recenzija hotela je sve važnije za inteligentno upravljanje tržištem i poboljšanje kvaliteta usluge. |
| n9 | This study focuses on categorising hotel reviews into positive and negative sentiments using a range of sentiment analysis models. | n9 | Ovo istraživanje se fokusira na kategorizaciju recenzija hotela na pozitivne i negativne komentare koristeći niz modela analize osećanja. |