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NLTK has been called “a wonderful tool for teaching, and working in, computational linguistics using Python,” and “an amazing library to play with natural language. Getting the data we’re going to use ready. In [1]:. Libraries to help with reading and manipulating data import numpy as np import pandas as pd libraries for visualizations import seaborn as sns import matplotlib. In [2]:. You’ll need to install NLTK if you don’t have it already! In [3]:. Let’s use the NLTK library import nltk from nltk.
Where are the files that we’re downloading? In [4]:. In [5]:. In [6]:. In [7]:. We can divide apply the string to both files with the objective of converting them into a lists. In [8]:. In [9]:. Checking tweets in the position 6 from both lists. In [10]:. In [11]:. Since we’ve checked that we have now two lists, we can get the amount of positive and negative tweets that we have available for our analysis.
In [12]:. Positive tweets: Negative tweets: In [13]:. In [14]:. We will merge the positive and negative tweets into one dataset to handle the data in a better and simpler way.
We’ll add tags for each kind of tweet. Positive tweets: pos and negative tweets: neg. Steps: Create a new column to identify both, positive and negative tweets. Call this new column sentiment. Do this for both DataFrames. In [15]:. How do the positive tweets look like? In [16]:. How do the negative tweets look like? In [17]:. Merging the DataFrames to have both, positive and negative tweets in one DataFrame. In [18]:. In [19]:. Adding the negative tweets to our new DataFrame “tweets”.
In [20]:. In [21]:. Let’s visualize and verify that our data is consistent. In [22]:. Engaging in text processing allows us to move onto more difficult tasks which are unique to dealing with text What is text processing? There are a whole host of powerful libraries dedicated to this, including: string and str. For an easier text manipulation we will convert any string to lowercase. We will remove special characters and any strings that are not going to be needed for further analysis.
String module Cleaning the tweets before going though any other text manipulation is helpful. In [23]:.
Before we start, let’s create a copy of our data so we can compare all the changes later. Converting any uppercase string to lowercase. In [24]:. In [25]:. In [26]:. 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]:. Software Images icon An illustration of two photographs.
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In [4]:. How do the negative tweets look like? Positive tweets: pos and negative tweets: neg. Reviewing the tweets that include URL’s.❿
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