Explore social influence

作者：

To explore the social influence of NFT, we first explore the global popularity of the NFT market and highlight key events using an interactive timeline. Then we look at specific collections to explore social attitudes using sentiment analysis, taking the Bored Ape Yacht Club (BAYC), the top-selling collection, as an example.

The global popularity of NFT

Among the most popular topics related to NFT searches over the past year, it indicates that “nft meaning”, “what is nft” and “opensea” remain at the top. While the overall market fever has leveled off, the bear market has seen a steady stream of beginners learning what NFT is and what it can do for them.

2021-2022 NFT highlighting events

The Breaking News of NFT of 2021-2022

Since 2021-2022 is an important period for the rapid development of nft, we use a timeline approach to cross-query he key nft events during this period, click the button to explore it by yourself. Some well-known events include:

On March 11th, 2021, Beeple’s artwork sold for $69 million, which marked a significant milestone for digital art and NFTs.

On April 30th2021, Yuga Labs launched Bored Ape Yacht Club, and it soon became one of the biggest NFT crazes of all time.

By exploring the big moments of NFTs using the timeline, it is able to identify patterns and trends of NFT and the impacts on the art, music, and sports industries, etc.

Sentiment Analysis

作者：

In this section, we will examine tweets related to one of the most popular NFT collections, Bored Ape Yacht Club. By analyzing public opinions of this collection, we can gain a better understanding of the public sentiment towards it.

Methodology

1.       Scrape the data about tweets that include “BoredApeYC” (Due to the limitation of Twitter API, we cannot access to the tweets directly, thus we use the scraped data from KAGGLE for substitution)

2.       Divide the file into sections that are readable by the Nocode Function website

3.       Classify the text into different sentimental categories

4.       Calculate the proportion of each category

5.       Import VaderSentiment and SentimentIntensityAnalyzer and calculate the sentiment score for each tweet, and sort them out by descending order

6.       Analyze the word cloud of each sentiment category and identify high-frequency keyword

7.       Find out the reasons for the significant difference in public attitudes towards the BAYC collection

World Cloud of All Text

After cleaning and recoding the data, I generated a word cloud of the text. The resulting graph is shown below.

From the graph, we can see that the words frequently mentioned with BoredApeYC can be divided into the following categories:

 

l  Main accounts of NFT

l  Producers/Artists related to the BoredApeYC collection

l  Platform: OpenSea

l  Descriptive words: token, NFT, project

l  Sentimental word: love

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Sentiment Analysis by Categories

Most of the public does not have a clear emotional trend towards this collection. However, compared to a small group of people, the overall emotional direction is positive.

Sentiment Score Calculation

Next, I sorted all the tweets into separate files based on the previously categorized types of emotions, and calculated the sentiment score for each tweet.

Steps

l  VaderSentiment

l  SentimentIntensityAnalyzer

Positive Tweets

Word Cloud of Positive Tweets

Most Frequent Words in positive tweets

Words such as “love”, “thank”, “great”, “best”, “amazing”, “happy”, and “awesome” suggest positive emotions or gratitude towards BAYC. Words such as “project”, “team”, and “gift” suggest expectations or eagerness to participate in this project. While frequent words like “leave”, “hope”, and “save” may be a little bit far from this subject, we guess it is a way of preaching. It can also prove that BAYC is popular among the public.

Why do people like BAYC?

Apart from quantitative analysis, we also conducted qualitative analysis on the tweets in this category. We propose that BAYC fans might treasure this collection because of its Investment Potential, Unique and Creative Artwork, and the good feelings it brings.

Negative Tweets

Word Cloud of Negative Tweets

Most Frequent Words in negative tweets

As for negative tweets, “miss” is the most frequently mentioned word, which indicates that people may generate negative attitudes because of missing certain opportunities or events. Words such as “project”, “chance”, and “need” imply that some people believe that BAYC needs some improvements in the future.

Why do people don't like BAYC?

Based on the market’s reaction to NFTs and a sentiment analysis performed on a dataset of tweets, it is clear that public attitudes towards NFTs are polarized. On one hand, some people have positive feelings towards NFTs due to their potential investment value or unique artistic features, while on the other hand, some people have biases against NFTs due to negative reports (such as ethical or security concerns). These significant differences in ideas mainly revolve around two points: the commercial value behind NFTs and the aesthetic presentation of this emerging art form.

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