Book: Text Analytics with Python

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By the end of this article, you will be able to perform text operations by yourself. Generally, we convert them in lowercase. This article is a short write-up of my experience with the. In sociology, we might think about this in a regression framework.

Importing Modules Python has built-in all the punctuation you need to account for in all cases. The incentives for making sense out of test data and utilizing it is huge as text sources are unlimited and have widespread applications from exams in a university to drug reports to tweets and messages on social media. It is defined as a field of Artificial Intelligence which enables computers to analyze and understand the human language.

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Introduction One of the biggest breakthroughs required for achieving any level of artificial intelligence is to have machines which can process text data. Thankfully, the amount of text data being generated in this universe has exploded exponentially in the last few years. It has become imperative for an organization to have a structure in place to mine actionable insights from the text being generated. From social media analytics to risk management and cybercrime protection, dealing with text data has never been more important. In this article we will discuss different feature extraction methods, starting with some basic techniques which will lead into advanced Natural Language Processing techniques. We will also learn about pre-processing of the text data in order to extract better features from clean data. Text analytics with python the end of this article, you will be able to perform text operations by yourself. In the entire article, we will use the twitter sentiment from the datahack platform. The basic intuition behind this is that generally, the negative sentiments contain a lesser amount of words than the positive ones. Here, we calculate the number of characters in each tweet. This is done by calculating the length of the tweet. This can also potentially help us in improving our model. But sometimes calculating the number of stopwords can also give us some extra information which we might have been losing before. This also helps in extracting extra information from our text data. 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. Basic Pre-processing So far, we have learned how to extract basic features from text data. B efore diving into text and feature extraction, our first step should be cleaning the data in order to obtain better features. We will achieve this by doing some of the basic pre-processing steps on our training data. This avoids having multiple copies of the same words. Therefore removing all instances of it will help us reduce the size of the training data. For this purpose, we can either create a list of stopwords ourselves or we can use predefined libraries. Our timelines are often filled with hastly sent tweets that are barely legible at times. In that regard, spelling correction is a useful pre-processing step because this also will help us in reducing multiple copies of words. To achieve this we will use the textblob library. Therefore, just for the purposes of learning, I have shown this technique by applying it on only the first 5 rows. Moreover, we cannot always expect it to be accurate so some care should be taken before applying it. We should also keep in mind that words are often used in their abbreviated form. We should treat this before the spelling correction step, otherwise these words might be transformed into any other word like the one shown below: 2. In our example, we have used the textblob library to first transform our tweets into a blob and then converted them into a series of words. It makes use of the vocabulary and does a morphological analysis to obtain the root word. Therefore, we usually prefer using lemmatization over stemming. Advance Text Processing Up to this point, we have done all the basic pre-processing steps in order to clean our data. Unigrams do not usually contain as much information as compared to bigrams and trigrams. The basic principle behind n-grams is that they capture the language structure, like what letter or word is likely to follow the given one. The longer the n-gram the higher the nthe more context you have to work with. Optimum length really depends on the application — if your n-grams are too short, you may fail to capture important differences. Instead, sklearn has a separate function to directly obtain it: from sklearn. The intuition behind this is text analytics with python two similar text fields will contain similar kind of words, and will therefore have a similar bag of words. Further, that from the text alone we can learn something about the meaning of the document. For implementation, sklearn provides a separate function for it as shown below: from sklearn. Here, we only extract polarity as it indicates the sentiment as value nearer to 1 means a positive sentiment and values nearer to -1 means a negative sentiment. This can also work as a feature for building a machine learning model. The underlying idea here is that similar words will have a minimum distance between their vectors. Word2Vec models require a lot of text, so either we can train it on our training data or we can use the pre-trained word vectors developed by Google, Wiki, etc. Here, we will use pre-trained word vectors which can be downloaded from the website. There are different dimensions 50,100, 200, 300 vectors trained on wiki data. For this example, I have downloaded the 100-dimensional version of the model. You can refer an article to understand different form of word embeddings. The first step here is to convert it into the word2vec format. End Notes I hope that now you have a basic understanding of how to text analytics with python with text data in predictive modeling. These methods will help in extracting more information which in return will help you in building better models. Did you find this article helpful. We prefer small values of N because otherwise our model will become very slow and will also require higher computational power. Shubham Good day — Thank you for the example. It provides good guidelines to newbies like me. I was able to follow your example right up til 3. Finally, the numerical sections following are not labeled correctly. The code seems to be fine with me. And the output is also correct. Try to follow the preprocessing steps properly and then run it again. As far as the numbering of sections is concerned, they were just mistakenly put by me. Not a big issue though since it is clear from the table of content. Still, I have updated it. Regards, Shubham Hi Shubham, great tutorial. Only thing is that I´m getting stuck at the same point 3. Hi Shubham, Thank you for the article. It is really helpful for text text analytics with python. Could you be able to make an text analytics with python of it. We only publish awesome content. We will never share your information with anyone.

Word count, presence of parts of words, sentence complexity, use of the passive voice, presence of emoticons, or any other text attribute that can be expressed as a number can be included as a feature. 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. We can use tf-idf value from information retrieval to get the list of key words. Network map of a subset of ericbrown. Other common things to do are words, which attempts to find the root of the word e. ConditionalFreqDist genre, len word for genre in brown. Converting to Lowercase In order to make tokens generated similar and proper, we need to align them all in one of the cases. A quick look at Amazon, Flipkart product reviews page shows the desired changes. Here, we only extract polarity as it indicates the sentiment as value nearer to 1 means a positive sentiment and values nearer to -1 means a negative sentiment.