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Play along with your songs. Easy to teach to kids and adults alike. Best Piano app in the market. The simplest one available to get started with Piano. Reviewing the tweets that include URL’s. In [27]:. Looking at the datapoint with index 0 to confirm that it has an URL. Removing URL’s from tweets. In [28]:. In [29]:. In [30]:. In [32]:. We will use the library emot, which is open source.

In [33]:. In [34]:. In [35]:. Converting emojis into words. In [36]:. Convert emoticons into words. In [37]:. In [38]:. Replacing emojis and emoticons from the tweets.

In [39]:. Removing mentions. For example mariacamila In [40]:. Removing any noise that might be left: Special characters. In [41]:. In [42]:. What are stopwords? What are the languages available? In [43]:. In [44]:. Tokenization consists of dividing a piece of text into smaller pieces.

We can divide paragraph into sentences, sentence into words or word into characters. How do we understand the meaning of a sentence? Why is important? It is important because before doing a text analysis we to identify the words that constitute a string of characters. It’s also important because we can identify the different type of words after obtaining the tokens. In [45]:. Let’s review an example before applying it to our DataFrames. But this is absurd.

Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. In [46]:. In [47]:. We can check how many sentences we got from the first text we have used for this example: print “The number of sentences is” , len sentences.

The number of sentences is 7. The patterns can be chosen from the regular expressions. Click here to learn more. In [48]:. In [49]:. How many substrings did we get? The number of substrings is In [50]:. In [51]:. In [52]:. In [53]:. Let’s try the tokenizers for our tweets. Which one would be better for the following text processing steps? Is the combination of some characters useful for analyzing tweets?

In [54]:. Name: tweets, Length: , dtype: object. In [55]:. In [56]:. Before getting the tokens from our tweets, we will proceed to remove the stop words. In [57]:. In [58]:. Let’s check how many tokens do we get from each tweet after the tokenization.

In [59]:. After getting the length for each tweet, we can check the distribution for the tweet’s length. In [60]:. In [61]:. In [62]:. In [63]:. In [64]:. Counter A Counter is a container that tracks how many times equivalent values are added. It can be used to implement the same algorithms for which other languages commonly use bag or multi-set data structures.

In [65]:. In [66]:. In [67]:. Sentence: [‘I’, ‘was’, ‘thinking’, ‘about’, ‘geometry’, ‘while’, ‘I’, ‘was’, ‘enjoying’, ‘the’, ‘best’, ‘month’, ‘of’, ‘winter’] Sentence with stemming: i wa think about geometri while i wa enjoy the best month of winter.

In [68]:. Lemmatization reduce words to their root form. For example, if the tag starts with NN, the token is assigned as a noun. In [69]:. Sentence: [‘I’, ‘was’, ‘thinking’, ‘about’, ‘geometry’, ‘while’, ‘I’, ‘was’, ‘enjoying’, ‘the’, ‘best’, ‘month’, ‘of’, ‘winter’] Sentence with lemmatizing: I wa thinking about geometry while I wa enjoying the best month of winter.

In [70]:. Run this cell to learn more about the tags nltk. In [71]:. In [72]:. In [73]:. In [74]:. In [75]:. In [76]:. In [77]:. In [78]:. Double checking the differences between Stemming and Lemmatization. In [79]:. Word Stemmer Lemma The the The idea idea idea behind behind behind digital digit digital computers comput computer may may may be be be explained explain explained by by by saying say saying that that that these these these machines machin machine are are are intended intend intended to to to carry carri carry out out out any ani any operations oper operation which which which could could could be be be done done done by by by a a a human human human computer comput computer.

In [80]:. In [81]:. In [82]:. In [83]:. In [84]:. First words: [‘always’, ‘amp’, ‘andry’, ‘andry pouting’, ‘back’, ‘birthday’, ‘cant’, ‘come’, ‘could’, ‘day’, ‘didnt’, ‘dont’, ‘even’, ‘face’, ‘face smiley’, ‘face smileyconfusion’, ‘feel’, ‘follow’, ‘follow back’, ‘followed’, ‘friday’, ‘frown’, ‘frown sad’, ‘fun’, ‘get’, ‘go’, ‘going’, ‘good’, ‘got’, ‘great’, ‘guys’, ‘happy’, ‘happy face’, ‘hey’, ‘hi’, ‘home’, ‘hope’, ‘ill’, ‘im’, ‘ive’, ‘kik’, ‘know’, ‘last’, ‘let’, ‘like’, ‘lol’, ‘looking’, ‘lot’, ‘love’, ‘lt’, ‘make’, ‘miss’, ‘morning’, ‘much’, ‘na’, ‘need’, ‘never’, ‘new’, ‘next’, ‘nice’, ‘night’, ‘no’, ‘not’, ‘oh’, ‘one’, ‘people’, ‘please’, ‘pouting’, ‘really’, ‘right’, ‘sad’, ‘sad andry’, ‘see’, ‘sleep’, ‘smiley’, ‘smileyconfusion’, ‘snapchat’, ‘someone’, ‘soon’, ‘sorry’, ‘still’, ‘thank’, ‘thanks’, ‘thats’, ‘think’, ‘time’, ‘today’, ‘us’, ‘via’, ‘wan’, ‘wan na’, ‘want’, ‘week’, ‘weekend’, ‘well’, ‘wish’, ‘work’, ‘would’, ‘yes’, ‘youre’].

In [85]:. In [86]:. In [87]:. In [88]:. Preparing the data for Modeling. In [89]:. In [90]:. In [91]:. MultinomialNB Accuracy: 0. Can we improve the bag of words model? Term frequency: This summarizes how often a word appears within a documents. In [92]:. In [93]:. In [94]:. First 50 words: [‘aa’, ‘aaaaaaaaaaa’, ‘aaaaaaaaaaaaa’, ‘aaaaaaaaaaaah’, ‘aaaaaand’, ‘aaaaages’, ‘aaaaahhhhhhhh’, ‘aaaahh’, ‘aaaahhh’, ‘aaahhh’, ‘aah’, ‘aahhhh’, ‘aaj’, ‘aameenall’, ‘aameenhappy’, ‘aamir’, ‘aapke’, ‘aaron’, ‘aarwwi’, ‘aasramany’, ‘aayegi’, ‘ab’, ‘abandoned’, ‘abandoning’, ‘abbeytaylor’, ‘abby’, ‘abbycan’, ‘abbymill’, ‘abes’, ‘abhi’, ‘abi’, ‘abit’, ‘abla’, ‘able’, ‘abligaverins’, ‘aboard’, ‘abouty’, ‘abp’, ‘abroad’, ‘abrupt’, ‘abs’, ‘abscess’, ‘absent’, ‘absolute’, ‘absolutely’, ‘abstinence’, ‘abt’, ‘abu’, ‘abudhabi’, ‘abusive’].

In [96]:. In [97]:. NLP is significant in light of the fact that it helps settle ambiguity in language and adds valuable numeric structure to the information for some downstream applications, for example, speech recognition or text analytics.