Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
We study investor sentiment on a non-classical asset, cryptocurrencies using a “cryptospecificlexicon” recently proposed in Chen et al. (2018) and statistical learning methods.We account for context-specific information and word similarity by learning word embeddingsvia neural network-based Word2Vec model. On top of pre-trained word vectors, weapply popular machine learning methods such as recursive neural networks for sentencelevelclassification and sentiment index construction. We perform this analysis on a noveldataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platformStockTwits during the period between March 2013 and May 2018. The constructed sentiment indices are value-relevant in terms of its return and volatility predictability for thecryptocurrency market index.
sentiment analysis lexicon social media word embedding deep learning