Sentiment Analysis is a field of Natural Language Processing that addresses the problem of
extracting sentiment or, more generally, opinion from the text. Obtaining deeper knowledge
on this topic can be beneficial for a wide range of scientific fields. In this approach, we will
use different tools and develop a web-based framework that enables and magnifies the
analyzing process, producing at the same time information about the cryptocurrency field in
dashboards and charts. This helps researchers and investors to classify, compare, and evaluate
their studies about trading. The analysis is conducted on cryptocurrency title news data,
scraped from Reddit and RSS feeds. Moreover, posts from Twitter were also obtained to
detect and measure the sentiment of specific coins in real time. By filtering and analyzing the
data using some Natural Language Processing tools and lexicons, their sentiment is
determined by the emotion found in the data collected from Reddit, RSS feeds, and Twitter.
Following the implementation of the pre-processing and normalization of the dataset
gathered, using the open-source library, Dash and Plotly library, we will create a web-based
interactive dashboard-framework that indicates cryptocurrencies’ prices and sentiment in
different figures, from the data collected from these sources in real-time. To achieve this, the
iteratively collected data in real-time will be discarded immediately after their process.
Collections
Show Collections
