Train = train.apply(lambda x: " ".join(x for x in x.split() if x not in stop)) For this purpose, we can either create a list of stopwords ourselves or we can use predefined libraries. train = ('','')Īs you can see in the above output, all the punctuation, including ‘#’ and has been removed from the training data.Īs we discussed earlier, stop words (or commonly occurring words) should be removed from the text data. Therefore removing all instances of it will help us reduce the size of the training data. The next step is to remove punctuation, as it doesn’t add any extra information while treating text data. train = train.apply(lambda x: " ".join(x.lower() for x in x.split())) For example, while calculating the word count, ‘Analytics’ and ‘analytics’ will be taken as different words. This avoids having multiple copies of the same words. The first pre-processing step which we will do is transform our tweets into lower case. We will achieve this by doing some of the basic pre-processing steps on our training data.
B efore diving into text and feature extraction, our first step should be cleaning the data in order to obtain better features.
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So far, we have learned how to extract basic features from text data. For example, train = train.apply(lambda x: len())Īnger or rage is quite often expressed by writing in UPPERCASE words which makes this a necessary operation to identify those words. It does not have a lot of use in our example, but this is still a useful feature that should be run while doing similar exercises. Just like we calculated the number of words, we can also calculate the number of numerics which are present in the tweets. Here, we make use of the ‘starts with’ function because hashtags (or mentions) always appear at the beginning of a word. This also helps in extracting extra information from our text data. One more interesting feature which we can extract from a tweet is calculating the number of hashtags or mentions present in it. Here, we have imported stopwords from NLTK, which is a basic NLP library in python.
But sometimes calculating the number of stopwords can also give us some extra information which we might have been losing before. Generally, while solving an NLP problem, the first thing we do is to remove the stopwords. Train = train.apply(lambda x: avg_word(x)) Return (sum(len(word) for word in words)/len(words)) Here, we simply take the sum of the length of all the words and divide it by the total length of the tweet: def avg_word(sentence): This can also potentially help us in improving our model. We will also extract another feature which will calculate the average word length of each tweet. Note that the calculation will also include the number of spaces, which you can remove, if required. This is done by calculating the length of the tweet. Here, we calculate the number of characters in each tweet. This feature is also based on the previous feature intuition. To do this, we simply use the split function in python: train = train.apply(lambda x: len(str(x).split(" "))) The basic intuition behind this is that generally, the negative sentiments contain a lesser amount of words than the positive ones. One of the most basic features we can extract is the number of words in each tweet.
Note that here we are only working with textual data, but we can also use the below methods when numerical features are also present along with the text. In the entire article, we will use the twitter sentiment dataset from the datahack platform. So let’s discuss some of them in this section.īefore starting, let’s quickly read the training file from the dataset in order to perform different tasks on it. We can use text data to extract a number of features even if we don’t have sufficient knowledge of Natural Language Processing.
Term Frequency-Inverse Document Frequency (TF-IDF).Basic feature extraction using text data.In addition, if you want to dive deeper, we also have a video course on NLP (using Python).īy the end of this article, you will be able to perform text operations by yourself. We will also learn about pre-processing of the text data in order to extract better features from clean data. In this article we will discuss different feature extraction methods, starting with some basic techniques which will lead into advanced Natural Language Processing techniques. From social media analytics to risk management and cybercrime protection, dealing with text data has never been more important. It has become imperative for an organization to have a structure in place to mine actionable insights from the text being generated. Thankfully, the amount of text data being generated in this universe has exploded exponentially in the last few years. One of the biggest breakthroughs required for achieving any level of artificial intelligence is to have machines which can process text data.
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