## 3 Materials and Methods
### Dataset Development  
Twitter Application Programming Interface (API) is used to collect tweets as raw data, and the whole procedure is explained in Figure 1. There are various Twitter scraping APIs available, each with a somewhat different set of capabilities, including the widely used Twitter API for tweet retrieval. To utilize the Twitter API, a user must first apply for developer access. Apart from scraping tweets, the Twitter API is capable of performing a variety of other functions. The Twitter API has several limits based on the account tier. The Twitter API allows for data collection based on keywords, hashtags, dates, and locations. The Twitter API was used with keywords and hashtags to acquire a dataset of English tweets expressing people's thoughts regarding the value volatility of cryptocurrencies during a specific time period. The data collected are critical for machine learning since it helps the model recognize patterns, make judgments, and do other tasks. For supervised learning, a dataset with the label mapped to the input feature is required. The data includes the following attributes TweetID, ReTweetCount, TweetText, TweetLanguage, TweetSource, and UserID. The text-based sentimental classification based on a Twitter dataset is applied in the text of short messaging of the Twitter micro-blogs. Therefore, it is necessary to collect short Twitter messages. For Twitter, there was a character limit, limiting the length of a single brief message to only 140 characters. As a result, the end-user is compelled to convey data utilizing little words or sentences. The reason is to restrict and use the words of tiny messages as keywords. Twitter API offers the capability to retrieve such tiny messages for a specific retriever in the XML file format. In comparison, these texts based short messages are classified into thirteen diverse sentimental attributes as Not_Relevant, Neutral, Happy, Surprise, Happy_Surprise, Sad, Happy_Sad, Angry, Sad_Angry, Disgust, Sad_Disgust, Angry_Disgust, and Sad_Angry_Disgust. Figure 1: Twitter API access procedure. In machine learning, there are essentially two types of learning processes. The first is supervised learning, and the second is unsupervised learning. In supervised learning, the developer supplies a labeled dataset to the system to train it. Unsupervised learning is a technique in which the system discovers patterns on its own from the dataset. Regarding the present situation, a supervised mode of learning is much more pertinent due to the versatility of the dataset. The selected field data frequencies are depicted in Figure 2. So, the values have been selected as cut-off lower and upper values that maximize efficiency. Sentiment analysis is performed on tweets, which are unstructured writings containing slang terms, acronyms, and orthographic errors. They must be converted to a proper format by preprocessing procedures for the machine learning model to assess the texts and deliver trustworthy, high-accuracy outputs. Thus, preprocessing is a critical stage in natural language processing and consists of multiple stages depending on the language's nature and the analysis's objective. Due to cryptocurrencies' unique and dynamic character, researching tweets about them presents more significant hurdles than analyzing tweets about other well-known conventional currencies. The difficulties include spelling inconsistencies and a broad range of currencies that differs from regular currencies, which is essential for identifying text properties. Due to the high number of inherited irregularities, natural language processing lacks robust methods and resources for extracting cryptocurrency-related attitudes from the text. The following steps illustrate how the dataset was preprocessed, as clarified in Figure 3:  
* By manually removing extraneous tweets that contained advertisements or were unrelated to the topic of cryptocurrencies, the first dataset was reduced to 3085 tweets. * Elimination of non-English letters
* Eliminating emoticons, symbols, numerals, and the hashtag sign.