SEACrowd/indolem_sentiment

IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse. This dataset is based on binary classification (positive and negative), with distribution: * Train: 3638 sentences * Development: 399 sentences * Test: 1011 sentences The data is sourced from 1) Twitter [(Koto and Rahmaningtyas, 2017)](https://www.researchgate.net/publication/321757985_InSet_Lexicon_Evaluation_of_a_Word_List_for_Indonesian_Sentiment_Analysis_in_Microblogs) and 2) [hotel reviews](https://github.com/annisanurulazhar/absa-playground/). The experiment is based on 5-fold cross validation.