Analyzing　and　understanding　emotional　expressions　in　user　comments　is　a　crucial　and　complex　task　in　business.　Conducting　text　sentiment　analysis　is　of　great　significance.　This　paper　constructs　a　novel　DIBTBL　model　that　integrates　an　extended　sentiment　dictionary,　an　improved　swarm　intelligence　algorithm,　and　deep　learning　techniques　to　accurately　capture　and　analyze　user　emotions.　Firstly,　this　paper　expands　the　sentiment　dictionary　to　extract　emotion　feature　words　from　the　review　text.　Secondly,　the　BERT　model　embeds　these　emotional　feature　words　and　pre-processed　text　into　high-dimensional　semantic　space　to　obtain　richer　semantic　representations　and　improve　sentiment　classification　performance.　Then,　the　TextCNN-BiLSTM　feature　extraction　model　is　established　to　balance　the　grasping　ability　of　local　and　global　features.　Fourthly,　this　paper　innovatively　improves　the　swarm　intelligence　algorithm　BWO　to　optimize　the　parameters　of　the　TextCNN-BiLSTM.　Finally,　MLP　is　employed　for　sentiment　classification.　The　experimental　data　is　crawled　from　Ctrip,　China＇s　largest　hotel　booking　website.　In　the　comparative　experiment,　the　proposed　model　achieves　a　higher　accuracy　than　TBL,　DTBL,　BTBL,　and　IBTBL　by　2.94%,　2.44%,　1.64%,　and　0.66%,　respectively.　In　addition,　we　compare　the　proposed　model　with　seven　advanced　models　in　the　open　dataset　waimai_10k.　The　experimental　results　indicate　that　this　model　outperforms　all　the　other　models,　with　an　average　improvement　in　accuracy　of　8.21%.　The　study　offers　precise　insights　into　user　sentiment,　assisting　companies　in　better　understanding　and　meeting　customer　needs.