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Please don’t use this as a tool to learn Japanese words. Once we’ve identified them, we will remove the URL’s. Given that we are aiming to perform a Sentiment Analysis, we don’t want to remove the negative stopwords because it could impact our detection of any negative sentiment. Before removing the stop words from our tweets, let’s review what is Tokenization. We read each word, interpret its meaning, and read the next word until we find an end point.

This is the reason why tokenization exists. If we want to create a model, the model might need all the words that make up the sentence separately. If instead on a sentence we have a paragraph, then we need to get all the sentences and out of all these sentences, we need to get the words.

At that point we can move forward to perform any kind of prediction. What is Tokenization? String tokenization is a process where a string is broken into several parts or tokens.

NLTK has different tokenize methods that can be applied to strings according to the desire output. To serve our purpose, we would like to keep some combination of characters as they can reference emojis and therefore, they can reference emotions.

The Collections module implements high-performance container datatypes beyond the built-in types list, dict and tuple and contains many useful data structures that you can use to store information in memory. Stemming is the process of removing prefixes and suffixes from words so that they are reduced to simpler forms which are called stems. In lemmatization, the speech part of a word must be determined first and the normalization rules will be different for different parts of the speech, whereas, the stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words that have different meanings depending on part of the speech.

A “tag” is a case-sensitive string that specifies some property of a token, such as its part of speech. Tagged tokens are encoded as tuples tag, token. This model allows us to extract features from the text by converting the text into a matrix of occurrence of words.

We will take our tweets that have been already processed, and the sentiment 1: Positive, 0: Negative. Then, we will proceed to create a list with the tweets and finally we will be able to use Countvectorizer.

Countvectorizer is a method to convert text to numerical data: It converts a collection of text documents to a matrix of token counts. TF-IDF allows for a simple mathematical way of defining word “importance”. This allows for a smarter document vector. Term frequency—inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

Inverse document frequency: This downscales words that appear a lot across documents in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus. Human language is astoundingly perplexing and diverse. NLP is an approach that helps us improve our communication and influence skills at a time these are becoming even more important. Even though computing systems enable fast and highly accurate communication channels, machines have never been good at understanding how and why we communicate in the first place.

What is NLP? NLP is a branch of artificial intelligence that allows computers to interpret, analyze and manipulate human language. NLP is about developing applications and services that can understand human languages. Alan Turing was part of this team. Part-of-speech tagging Named Entity Recognition NER Question answering Speech recognition Text-to-speech and speech-to-text Topic modeling Sentiment classification Language modeling Translation Information retrieval: Web searching algorithms that use keyword matching.

Any examples? Maybe Google? Target Ads: Recommendations based on key words from social media. Have you search for shoes, laptops, flowers? Later you’ll see some adds based on all those searchs. Text Summarization: Algorithms that allow getting a summary out of a text. Sentiment Analysis: Analysis done to reviews or posts from apps like Twitter, Yelp, Airbnb, Google reviews, etc, to understand human’s feelings and emotions.

Which libraries can we use? It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.

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.

 
 

Harvard CSA | Lecture Natural Language Processing

 
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