Data · dataset · 2025
A Bangla Language Movie Review Dataset with Sentiment and Emotion Labels
Listed in Teesside University Research Data Repository
This dataset contains 1,500 unique Bangla movie reviews, carefully collected and designed to support research in Sentiment Analysis and Emotion Detection.
Description
Each review is written in natural Bangla, reflecting the way people usually express their opinions on movies. To make the dataset more realistic, the reviews vary in length and tone — some are short one-line opinions, while others are longer and combine multiple expressions, just like real comments found on YouTube or social media.
Each review has been annotated with: Sentiment labels: Positive, Negative, or Neutral Emotion labels: Happy, Sad, Angry, Excited, Disappointed, or Neutral The dataset was built with special care to maintain logical consistency. For example, a review that expresses excitement about a movie is not labeled as Negative, and a clearly disappointed review is not marked as Positive. This makes the resource more reliable for training and evaluating natural language processing (NLP) models.
Links
Where it is published
- DOI doi.org/10.17632/48jjjrjvsn.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/48jjjrjvsn.1 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:460208 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Natural Language Processing'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |