A Dataset of Indonesian Tweets and Daily Market Data for Non-Blue-Chip Stocks on the Indonesia Stock Exchange

Description

This dataset provides paired Indonesian-language social media text and daily market data for 15 non-blue-chip stocks listed on the Indonesia Stock Exchange (IDX). The dataset is designed to support research on retail investor behaviour, social media sentiment, short-term stock price movements, and market euphoria in an emerging-market context. The dataset spans the period from 1 January 2022 to 31 December 2024 and consists of four Excel files. 1. Tweet Data (7,672 rows) - Contains cleaned and anonymised Indonesian tweets collected via targeted keyword queries on X (formerly Twitter) using the X API. - Each row includes: stock ticker, cleaned tweet text, posting date and time (created_at), engagement metrics (favorite_count, retweet_count, reply_count, is_quote_status), and a sentiment label. - The label was assigned manually by three annotators using majority voting, in four classes: neutral (3,490), positive (1,753), irrelevant (1,751), and negative (678). Irrelevant marks posts that state no position on the stock. - The same rows are provided in Indonesian (Tweet Data - ID.xlsx) and English (Tweet Data - EN.xlsx), with identical labels. 2. Market Data (10,830 rows) - Contains daily OHLCV (Open, High, Low, Close, Volume) records and the company name for the 15 stocks, downloaded from Yahoo Finance using the yfinance Python library. - This file holds active trading days only, 722 days per stock, without imputation. 3. Combined Multimodal Data (16,395 rows) - Integrates the daily market variables with aggregated daily social media metrics using a left-join operation by "Ticker" and "Date". - Missing values due to weekends, public holidays, or temporary suspensions have been forward-filled to maintain a continuous series of 1,093 calendar days per stock. - Includes the continuous OHLCV data, technical features (14-day RSI, daily price change percentage, daily volume change percentage), daily tweet counts, and average daily sentiment scores. - The average daily sentiment scores are computed from the manual labels (positive +1.0, neutral 0.0, negative -1.0), averaged per stock on each date. Tweets labelled as irrelevant are excluded, so this file summarises 5,921 tweets. The 15 stocks were selected based on high retail participation and speculative characteristics: AUTO, BRMS, BRPT, DSSA, FORU, IMAS, KARW, KONI, MLPT, PANI, PSAB, SGER, SRAJ, TOBA, and TPIA. All tweet texts have been cleaned and anonymised: usernames, user mentions (@), external URLs, hashtags, and personal identifiers have been removed to ensure user privacy. This resource can be used for: • Training and evaluating Indonesian NLP models for sentiment analysis • Developing short-term stock price forecasting systems • Multimodal experiments combining text embeddings with time-series market features • Investigating retail-driven euphoria and speculative behaviour in non-blue-chip stocks

Institutions

• Binus University

  Jakarta, Jakarta
• Padjadjaran University

  West Java, Bandung

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