Sentimental Analysis is one of the three analyses used to predict and analyze the crypto price trends. It is a type of analysis where traders know the opinions of important people in the crypto market such as influencers and other every day leading traders.
Following these successful traders, people nowadays are willing to invest in cryptocurrencies. Whereas there are some who are still in doubt seeing the volatility of the crypto market. The fluctuating prices and the risks involved make them hesitant. To avoid such situations of doubt and risks, traders first analyze the market.
Experts and professional traders know the right time to trade and invest. They study the price trends of the market and analyse what will happen next. The 3 ways of doing the crypto market analysis are Technical, Fundamental and Sentimental Analysis. These analyses help the traders to make the right decision for investment.
Most marketing and investment decisions in the virtual world are made after observing what influential reviewers and companions say about the product or service. The reviews and comments play a significant role in analyzing the market. These reviews, comments, and opinions are called Sentiment Data. The work of identifying and analyzing the data is known as Sentimental Data Analysis or Sentimental Analysis.
In simple words, Sentimental Analysis is where traders effectively try to observe the opinions, views, judgments, and evaluations of influential people in the crypto market like journalists and other prominent traders.
This analysis is tricky as the analysis can be based on people’s social media handles. So, when there is an increase in the price of the cryptocurrency, people will start sharing positive messages on their social media handles and vice versa.
Now, let us move towards the methods of analyzing the sentiment data.
Techniques of Analyzing Sentiment Data
There are different techniques to analyze the sentiment data. Some of these are:
- Document-level of Sentiment Analysis-
Thoughts and opinions are individual expressions of people. It describes people’s viewpoints, judgments or feelings towards a thing or an event. Many blogs, websites, discussions allow people to express their opinion in the form of reviews and comments.
In the document-level of sentiment analysis, each document concentrates on a single object or event. The document contains opinions from a single opinion holder. The opinion can be classified into two simple classes- Positive or Negative.
- Sentence-level of Sentiment Analysis-
Sentence-level of Sentiment Analysis gives a more refined and explained view of different opinions written in a document about the object.
In this level, those judgments which contain no opinion are filtered out and decided whether the opinion on the object is positive or negative.
- Aspect based sentiment analysis-
Document-level and Sentence level Sentiment Analysis works properly when they refer to a single object. In many cases, people talk about objects or entities that have multiple features or characteristics. They also have different opinions about different features. It usually occurs in product review and discussion.
Aspect based sentiment analysis concentrates on the identification of all sentimental expressions in a given document and the features to which the opinions refer.
- Comparative Sentiment Analysis-
In many cases, people express their opinions by comparing the object with a similar product. Hence, the aim here is to identify those sentences that include comparative opinions.
- Sentiment Lexicon Acquisition-
The sentiment lexicon acquisition analysis method uses a list of words and expressions that are used to express people’s individual feelings and opinions. It not only contains list of certain words but also idioms and phrases.
For example, Pen X is better than pen Y. This sentence does not express an opinion on any of the two pens, whether they are good or bad. So, these types of sentences or documents are furthered analyzed using 3 approaches:
- Manual Approach: This is time-consuming and hence usually not feasible.
- Dictionary-based approach: This approach uses “Word Net” of the sentiment word to conduct an analysis.
- Corpus-based approach: Here, a domain-specific Sentiment Lexicon is created to carry out the analysis.
Concluding, we learned about the different types of Sentimental Analysis and how we use it to predict market sentiments. The market sentiments helps in predicting the crypto price trends. This eventually will help the traders to know the right time to trade or invest in the cryptocurrency market.