( 11th October 2019 )
One of consideration of value creation by trading of cryptocurrency, we should consider each coin volatility of trading. The values of coin are influence the demand from market and volume of transaction for other currency or asset. Each cryptocurrency connects any type of assets near future but some particular usage or demands for special asset maybe generated by the voice of people. These peoples voice or market opinion maybe predictable. We design AI trading bot and AI market value prediction may become good tool to see the future value of cryptocurrency. Plus, create new trend of market or cryptocurrency can be generatable value. To do this action, trading bot and marketing bot should be design for these trading event. The design of tool should be use sentimental analysis technologies and its NLP based artificial sentence generator.
Sentiment Analysis has become an important field these days. A simple search on a popular search engine will reveal several important resources for this field. An important application of sentiment analysis is to determine the public opinion or sentiment about different crypto-currencies in SNS. Using sentiment analysis techniques on SNS crypto-currencies reputation can be judged from the public point of view.
This document proposes a Sentimental Analysis method for crypto-currencies using multiple input sources.
Data Sources for Analysis
Following data sources can be utilized for performing crypto-currency related sentimental analysis:
1) Reddit
2) YouTube Comments
3) Twitter
Tasks List (Sub-modules Identification)
1) Topic Identification (Text Categorization)
It is one of the important subtasks for sentimental analysis for crypto-currencies. Social media can contain data regarding multiple topics like sports, religion, and crypto-currency. Therefore, first we need to short list the relevant data using topic identification techniques or text categorization. This categorization can be done using semantic or machine learning techniques. Some of the subtasks involve tokenization, Natural Language Processing (NLP) and inference.
2) Custom Positive & Negative Words List
In addition to Senti-Wordnet we can create a custom list of positive and negative words for improving the sentiment score accuracy. For this we need to collect a list of words related to the crypto-currency domain.
3) Sentiment Analyzer:
After the identification of relevant data, each will be input into the sentiment analyzer. This sub-module will tokenize the provided input. Next for each token the sentiment will be calculated using the created positive and negative lists as well as the Senti-Wordnet. Next the sentiment score for each token will be added to calculate the overall sentiment of the given input. A flow diagram for the Sentimental Analysis has also been provided.
4) Front End Designing
User interface need to be designed for the sentimental analysis tool. A web based user interface can be designed or a desktop application can be created.
5) Integration & Testing
After the unit testing of each sub-module, the last step needs integration and overall testing for finding as much bugs as possible.
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