( 08th October 2019 )
The creation of Bitcoin is mysterious as it was created by a person or group of people using the name "Satoshi Nakamoto" and released in 2009. Along with the launch of Bitcoin "Satoshi Nakamoto" published a paper titled "Bitcoin: A Peer-to-Peer Electronic Cash System" which described a peer-to-peer payment system using electronic cash (cryptocurrencies) that could be sent directly from one party to another without the use of a third party to validate the transaction. This innovation is created by the use of the "blockchain" which is like a shared ledger on the peer-to-peer network where all transactions are verified by the network so they cannot be forged. The applications of blockchain technology go beyond peer-to-peer payment systems. Blockchain technology provides security, privacy, and a distributed ledger which makes them applicable for internet-of-things applications, distributed storage systems, healthcare, and more. The range of applications of the blockchain has led to many more blockchains and cryptocurrencies being created. Cryptocurrencies are tied to the blockchain because they provide the incentive for machines, and the electricity they consume, to run and validate the blockchain. As use of blockchains increases so too will the use of cryptocurrencies. This gives them an inherent value, but what that value is depends on many factors. Because this is a new type of currency, and store of value, improving the understanding of what can lead to price changes brings with it value.
Widely considered immutable time-stamped data structures, blockchains implement peer-to-peer networks where participants can verify interactions concurrently using decentralized peer-to-peer consensus protocols. As an emerging technology trend, different research and industrial perspectives are being assembled to document its potential disruptive impact.
Forecasting Cryptocurrency Value by Sentiment Analysis Blockchains has five unique characteristics, namely:
1) Peer-to-peer communication without a central authority.
2) Transparent transaction processing with optionally-disclosed ownership.
3) Decentralized transaction history verifiable by all participants.
4) Immutability of records assuring chronological sequence and accessibility.
5) Logic-based processing to trigger algorithms and events.
The aforementioned characteristics have made blockchain particularly suitable to manage cryptocurrencies: Electronic cash systems administered via peerto- peer consensus. Indeed, the most widely known for cryptocurrency, the Bitcoin, remains something like the gold Standard for financial blockchain applications. Nonetheless, while blockchains have been used extensively in financial entities, their decentralized immutability characteristics have made them particularly suitable for applications in other domains as diverse as Law, Food Traceability, and Open-source Software Management.
As the economic and social impact of cryptocurrencies continues to grow rapidly, so does the prevalence of related news articles and social media posts, particularly tweets. As with traditional financial markets, there appears to be a relationship between media sentiment and the prices of cryptocurrency coins. While there are many causes of cryptocurrency price fluctuation, it is worthwhile to explore whether sentiment analysis on available online media can inform predictions on whether a coin’s price will go up or down.
The ubiquity of Internet access has triggered the emergence of currencies distinct from those used in the prevalent monetary system. The advent of cryptocurrencies based on a unique method called “mining” has brought about significant changes in the online economic activities of users. Various cryptocurrencies have emerged since last decade, when Bitcoin was first introduced. Nowadays, cryptocurrencies are often used in online transactions, and their usage has increased every year since their introduction.
Cryptocurrencies are primarily characterized by fluctuations in their price and number of transactions. For instance, the most famous cryptocurrency, Bitcoin, had witnessed no significant fluctuation in its price and number of transactions until the end of 2013, when it began to garner worldwide attention, and witnessed a significant rise and fluctuation in its price and number of transactions. Other cryptocurrencies—Ripple and Lite coin, for instance—have shown significantly unstable fluctuations since the end of December 2013. Such unstable fluctuations have served as an opportunity for speculation for some users while hindering most others from using cryptocurrencies.
Research on the attributes of cryptocurrencies has made steady progress but has a long way to go. Most researchers analyze user sentiments related to cryptocurrencies on social media, e.g., Twitter, or quantified Web search queries on search engines, such as Google, as well as fluctuations in price and trade volume to determine any relation. Past studies have been limited to Bitcoin because the large amount of data that it provides eliminates the need to build a model to predict fluctuations in the price and number of transactions of diverse cryptocurrencies.
Therefore, we are going to implement a method to predict fluctuations in the price of cryptocurrencies. The proposed method analyzes user comments on online cryptocurrency communitiesi.e on twitter, and conducts an association analysis between these comments and fluctuations in the price to extract significant factors and formulate a prediction model. The method is intended to predict fluctuations in cryptocurrencies based on the attributes of online communities and their prices in crypto exchanges.
It is estimated that 90% of the data in the world has been created in the last two years. Much of that data is in the form of unstructured text data whether it be in the form of tweets, articles posted to the internet, text messages, emails, or other forms. This vast amount of unstructured data has led to the creation of "natural language processing" (NLP) as an area of study or development. NLP is a collection of methods for computers to analyze and understand text.
Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Usually, besides identifying the opinion, these systems extract attributes of the expression e.g.:
Polarity: if the speaker expresses a positive or negative opinion,
Subject: the thing that is being talked about,
Opinion holder: the person, or entity that expresses the opinion.
Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media.
With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service.
Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, and feelings toward a subject or topic.
Sentiment analysis, just as many other NLP problems, can be modeled as a classification problem where two sub-problems must be resolved:
Ø Classifying a sentence as subjective or objective, known as subjectivity classification.
Ø Classifying a sentence as expressing a positive, negative or neutral opinion, known as polarity classification.
In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features. It could also be a product, a cryptocurrency, a service, an individual, an organization, an event, or a topic.
Our system will to associate a particular input to the corresponding output based on the pattern in which the particular cryptocurrency is sustaining. Our ultimate goal is to refine this price prediction model and incorporate it into a larger system that automatically and intelligently manages a cryptocurrency portfolio.
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