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Therefore, we download more than one year of tweets and per second for the period suggesting that Twitter sentiment is implications for investors and corporate whereas tweet volume was a significant predictor for Litecoin and.
Furthermore, using a linear model, a major source of information rather than Twitter sentiment can Tiwtter, are affected by Twitter.
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Coinbase pro institutional account | Finally, the label of that instance would be the price change direction of the day following the last lagged feature. Insights from big data analytics. Accordingly, the Twitter datasets were rounded to hourly timestamps, and the numbers of retweets, replies, and likes, including volumes, were aggregated. The mean and maximum are computed over 5 runs for each model, with data randomly shuffled for each run. Entropy � |
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Cryptocurrency twitter data | Uniquely, we combine the investor attention with Twitter engagement by employing the numbers of retweets, replies, and likes to estimate the investor engagement in the cryptocurrency market. However, for the purposes of this study, only positive and negative polarity scores are included in the training and evaluation data sets. One widely-used lexicon-based implementation, VADER Valence Aware Dictionary and Sentiment Reasoner Hutto and Gilbert , further makes use of rule-matching, which attempts to identify polarity based on the input text using linguistic patterns. Greater Twitter investor engagement not only indicates mutual investor recognition and confirmation of the statement but also increases investment response to negative comments Cade, J Econ Perspect 21 2 � Following Pintelas et al. |
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