blog posts

Emotion

What is emotion analysis and how is it done?

Many traders think that we have only two kinds of analysis in financial markets: fundamental and analysis technical analysis. But another type of analysis is used called View Analysis. This analysis applies not only to the trade but also to many locales. In this article, you will get familiarized with the study of emotions in simple language.

What is emotion analysis?

Emotion analysis is the method by which positive, negative, and neutral emotions present in a text, sentence, or phrase are recognized so that the future can be expected using emotion analysis.

Emotion analysis

Financial markets are full of fear, worry, and greed, which are the emotions that guide the market. If most people feel positive about the market or an asset, we should expect prices to rise, and if emotions are negative, we should probably expect prices to fall.

And also about the news published by the media. Information has a significant impact on the market trend. Naturally, if good news is published about Bitcoin or a stock, it can help prices rise, and on the other hand, negative information can lead to a fall.

So the job of emotion analysis is to use textual data to identify positive, negative, or neutral emotions in written data so that the analyst can make decisions about the future.

It should be noted that the financial market is just one of several areas in which emotion analysis is used. Researchers and scientists, product analysts, developers all use emotion analysis in their field to make better decisions for their business.

How is emotion analysis done?

Emotion analysis can be done manually by collecting written information. For example, when the analyst sees the phrase “bitcoin is ready to climb,” he considers it positive.

However, due to the abundance of data, emotion analysis is usually done automatically through machine learning algorithms in programming.

For example, a computer program is written in which the programmer states: If you see the words “bitcoin,” “Amazon,” and “acceptance” in a piece of content, consider that news or story as positive.

machine learning

Using techniques, natural language processing (NLP), the program learns to distinguish sentences through an algorithm that labels each sentence or phrase during the machine learning process.

Sentences and phrases are usually classified into positive, negative, and neutral categories.

For example, suppose a market analyst wants to know people’s general feelings about an asset (such as Bitcoin).

Using a data collection program, he collects 1 million texts, including the word bitcoin, from all over the Internet.

He now has to categorize these 1 million texts into three categories: positive, negative. And neutral, to be able to guess the dominant emotions of individuals. Since he can not read 1 million texts, he turns to programming, artificial intelligence, and machine learning, a subset of artificial intelligence.

Imagine the following five sentences are examples of user data:

1. Bitcoin is not growing anymore. It is about to fall. (Negative)
۲. Bitcoin will explode in a few months. (Positive)
3. I bought bitcoins. (Positive)
4. Do you think I should buy bitcoin? (Neutral)
5. Amazon bans bitcoin advertising. (Negative)

For example, he reads 1,000 texts and labels them positive, negative, or neutral. It then gives the raw data to the machine learning algorithm, and the algorithm learns from them what it should label for the rest of the data.

Analysts typically analyze emotions using programming languages, especially Python, data collection APIs, and ready-made models and algorithms. Designing advanced models for analyzing emotions requires a lot of time and knowledge.

How is emotion analysis done in the digital currency market?

Analysts first collect data using specific keywords related to their market (such as bitcoin). This data is in text form.

What is emotion analysis and how is it done?

Texts are usually collected through social networks (Twitter and Telegram groups) and news media.

For example, using Python programming and Twitter libraries, you can easily collect tweets with the keyword “Bitcoin.”

The data is then identified as positive, negative, or neutral using machine learning algorithms or a conventional dictionary-based algorithm. And the analyst can use that data to make decisions about future market trends.

In more advanced methods, each data is given a particular point, and, for example, the daily information is compared to the price trend of that day so that they can reach a comprehensive pattern.

Analysts sometimes link between their emotion analysis algorithms and trading bots to quickly place a buy or sell order in the market if they see news or positive/negative emotions.

For example, on April 1, 2019, the price of Bitcoin suddenly increased by more than 30%. That day coincided with April Fools’ Day, when the media spread false news. Many analysts believe that one of the main reasons for this sharp rise was the news of the approval of Bitcoin ETF, which was published by Finance Magnates. In this way, and according to this claim, the trading robots made a large purchase and increased the price based on this news and the analysis of emotions.

Conclusion

In addition to technical and fundamental analysis, emotion analysis determines the future trend of prices in financial markets. Of course, this type of analysis has many applications in other areas.

Emotion analysis is often done using programming and machine learning. In this type of analysis, the analyst collects a large amount of data from media and social networks and then determines whether any data is positive, negative, or neutral, thus deciding what to do next.

Source