DeepAI AI Chat
Log In Sign Up

Social Media Sentiment Analysis for Cryptocurrency Market Prediction

by   Ali Raheman, et al.

In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how the different sentiment metrics are correlated with the price movements of Bitcoin. For this purpose, we explore different methods to calculate the sentiment metrics from a text finding most of them not very accurate for this prediction task. We find that one of the models outperforms more than 20 other public ones and makes it possible to fine-tune it efficiently given its interpretable nature. Thus we confirm that interpretable artificial intelligence and natural language processing methods might be more valuable practically than non-explainable and non-interpretable ones. In the end, we analyse potential causal connections between the different sentiment metrics and the price movements.


page 1

page 2

page 3

page 4


FedNLP: An interpretable NLP System to Decode Federal Reserve Communications

The Federal Reserve System (the Fed) plays a significant role in affecti...

Causal Analysis of Generic Time Series Data Applied for Market Prediction

We explore the applicability of the causal analysis based on temporally ...

What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis

Market sentiment analysis on social media content requires knowledge of ...

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, ha...

Towards Financial Sentiment Analysis in a South African Landscape

Sentiment analysis as a sub-field of natural language processing has rec...

Investigating the Impact of 9/11 on The Simpsons through Natural Language Processing

The impact of real world events on fictional media is particularly appar...