The　metaverse　concept　extends　beyond　virtual　worlds　and　can　be　applied　to　collaborative　analysis　environments.　Data　analysts　worldwide　may　read　academic　article　extracts　in　real-time　in　a　shared　digital　workplace　to　mine　and　analyze　data　using　the　metaverse.　Furthermore,　a　semantic　metaverse　in　natural　language　processing　might　also　involve　creating　a　digital　environment　with　linguistic　and　semantic　connections.　Many　dedicated　researchers　navigating　the　complexities　of　natural　language　processing　in　the　metaverse　era　have　spent　considerable　time　searching　for　relevant　papers.　However,　online　reviews　and　evaluations　of　articles　are　helpful　for　their　assistance　and　may　save　the　researcher　time.　In　this　work,　human　specialists　manually　produced　a　dataset　from　four　conferences　and　evaluated　subjectively　using　rule-based　techniques.　Subsequently,　we　aim　to　evaluate　the　effectiveness　of　pre-trained　word　embeddings　and　pre-trained　BERT　models　seamlessly　integrated　with　convolutional　neural　networks.　This　endeavor　focuses　on　the　subjective　analysis　and　classification　of　contributions　and　previous　work　sentences　extracted　from　academic　literature.　For　comparison,　various　deep　learning　architectures　were　systematically　employed,　including　long　short-term　memory-GloVe　and　bi-directional　long　short-term　memory-GloVe,　alongside　classical　machine　learning　methods.　Our　findings　show　that　the　proposed　BERT　model　achieved　state-of-the-art　performance　in　classification　and　subjective　analysis　tasks　with　an　accuracy　of　91.50%　and　F1　score　of　91.00%.　Finally,　we　plan　to　utilize　sentence　similarity　to　identify　contributions　within　abstracts,　thus　outlining　potential　avenues　for　future　research.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.