( 22nd June 2019 )
I am developing sentimental analysis project for crypto currency these days. The approach of this task is same as NASDAQ stock exchange prediction model which I did five to six years ago. Now, crypto currency market is visit one, we implement this module for crypto currency model.
Following is the details of action of task.
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.
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.
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 in this proposal document.
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.
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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