As we know, there would be some meaningless string in texts such as “a”, “the” or some punctuations. Tokenization, Lemmazation or Stemming are the most common ways to separate the whole text.Īfter we split the texts into words, we could categorize them by their part of speech. Therefore, the whole passage of texts would be divided into specific text units such as a sentence or, more frequently, a word. While in text mining, the computer would automatically get rid of some useless information and quantify the useful texts by transforming them into numbers.įor text mining project, the computer couldn’t understand the semantics of the words so it could only recognize words based on the structure. Usually human would process texts in our brains by reading them line by line to understand and conclude them. In this case, another option is to use some 0-coding-needed web scraping tools such as Octoparse. But for those who don’t have a high-level programming skill or don’t understand web structure so well, programming seems to be the biggest obstacle to their projects. Libraries such as BeautifulSoup4, request or Tweepy have been widely used. Many people would write their own spiders using python or other languages to scrape data on websites. ![]() Nowadays, more people would prefer to build a web spider and scrape first-hand and up-to-date data from the internet. Text Acquisition is the first and the most important step before text mining.įor people who want to conduct a text mining project, they could find many open-source data from data platforms such as Kaggle. However, the datasets on such platforms have been widely used, so it is difficult to conduct a unique project based on these sources. In addition, there are some other typical text mining applications such as sentimental analysis, information extraction, topic modeling, etc.īefore doing a project with text mining skills, we need to first obtain raw data from somewhere. Text mining is based on Natural Language Processing (NLP) and combined with some typical data mining algorithms such as classification, clustering, neural network, etc. Text mining is a technique that could mine high-quality information among a large number of texts. That’s when text mining comes into being. Therefore, figuring out some way to extract only the useful information really matters at this moment. However, they would miss the 20% important information if they just ignore all of them. Some people start to get tired of information overload. Perhaps it would take several hours to go through all the news, emails or tweets every day even though 80% of them are not the information they need. Many people are plagued by information overload. It is said that by 2020, there would be 44 zettabytes of data in the entire digital universe.Īccording to Domo’s data never sleeps 7.0, an unbelievable amount of data is created every single minute: ![]() Undoubtedly, this is an age of information explosion.
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