Typically, whether we’re given the data or have to scrape it, the text will be in its natural human format of sentences, paragraphs, tweets, etc. The spam filter in your email and the spellcheck you’ve used since you learned to type in elementary school are some other basic examples of when your computer is understanding language.Īs a data scientist, we may use NLP for sentiment analysis (classifying words to have positive or negative connotation) or to make predictions in classification models, among other things. The fact that devices like Apple’s Siri and Amazon’s Alexa can (usually) comprehend when we ask the weather, for directions, or to play a certain genre of music are all examples of NLP. This is all changing though as advances in NLP are happening everyday. As you probably know, computers are not as great at understanding words as they are numbers. NLP is short for Natural Language Processing. import rpus nltk.download('stopwords') from rpus import stopwords stop = stopwords.words('english') data_clean = data_clean.apply(lambda x: ' '.join()) data_clean.NLP for Beginners: Cleaning & Preprocessing Text Data The code below uses this to remove stop words from the tweets. The Natural Language Toolkit (NLTK) python library has built-in methods for removing stop words. This would be particularly important for use cases such as chatbots or sentiment analysis. For example, if we were building a chatbot and removed the word“ not” from this phrase “ i am not happy” then the reverse meaning may in fact be interpreted by the algorithm. This includes any situation where the meaning of a piece of text may be lost by the removal of a stop word. There are other instances where the removal of stop words is either not advised or needs to be more carefully considered. This is particularly the case for text classification tasks. Stop words are commonly occurring words that for some computational processes provide little information or in some cases introduce unnecessary noise and therefore need to be removed.
import re def clean_text(df, text_field, new_text_field_name): df = df.str.lower() df = df.apply(lambda elem: \t])|(\w+:\/\/\S+)|^rt|http.+?", "", elem)) # remove numbers df = df.apply(lambda elem: re.sub(r"\d+", "", elem)) return df data_clean = clean_text(train_data, 'text', 'text_clean') data_clean.head() To keep a track of the changes we are making to the text I have put the clean text into a new column.
If we include both upper case and lower case versions of the same words then the computer will see these as different entities, even though they may be the same. We need to, therefore, process the data to remove these elements.Īdditionally, it is also important to apply some attention to the casing of words. All of which are difficult for computers to understand if they are present in the data. Text data contains a lot of noise, this takes the form of special characters such as hashtags, punctuation and numbers. One of the key steps in processing language data is to remove noise so that the machine can more easily detect the patterns in the data.
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