applied text mining in python assignment 1 solution github

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Applied Text Mining in Python

University of Michigan via Coursera Help

• Module 1: Working with Text in Python
• Module 2: Basic Natural Language Processing
• Module 3: Classification of Text
• Module 4: Topic Modeling

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran

• united states

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• WW Will Wheeler 6 years ago This class is rife with errors. The main problem is the very finicky autograder, which is frequently programmed incorrectly and often gives no useful feedback. Other problems include readings in the first week that rely on modules from later weeks… Read more This class is rife with errors. The main problem is the very finicky autograder, which is frequently programmed incorrectly and often gives no useful feedback. Other problems include readings in the first week that rely on modules from later weeks, incomplete instructions (e.g., how to break ties in a sorted list), and use of Python 2.7 in examples (although the class is in Python 3.5). At the beginning of the course (but not in the advertised materials), they emphasize "self-learning," which really means going to the discussion forums and using Google to look up errors. Because of the problems with the autograder and the emphasis on self-learning, estimated completion times are wildly inaccurate. The first week's assignment is beyond ridiculous, and people on the discussion forums report taking 20 or as much as 43 hours for a stated three-hour assignment! Helpful
• Raivis Joksts 6 years ago The topic is interesting, however as with the Machine Learning course from UM, this one suffers from too much theoretically focused graded assignments, and would benefit from more practical real life example tasks. Helpful

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Applied Text Mining in Python

Description.

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

based on 3331 ratings

Applied Data Science with Python

U-M Credit Eligible

V.G. Vinod Vydiswaran

Assistant Professor

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Text Mining using Python

Introduction.

Overview Time: min Objectives

What is Text Mining?

Text Mining is the process of deriving meaningful information from natural language text.

The overall goal is to turn texts into data for analysis via application of Natural Language Processing(NLP).

What is NLP?

Natural Language Processing(NLP) is a part of computer science and artificial intelligence which deals with human languages.

In other words, NLP is a component of text mining that performs a special kind of linguistic analysis that essentially helps a machine “read” text.

Tokenization

What is tokenization.

It is the process of converting large text to smaller chunks. Formally - “Token is a single entity that is building blocks for sentence or paragraph”. Tokenization is sometimes as simple as splitting the text into white spaces.

We are using punkt tokenizer. This tokenizer uses unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences in order to split text into token

Using tokenization to get count of words

What is stemming.

Stemming is the process of reducing tokens to root forms. For example - studying and studied are converted to study. There are two commonly used stemming techniques in python.

Porter Stemming

Lancaster stemming.

Among these, Lancaster stemming is more aggresive, with twice the rules as porter stemmer and tends to over stem words -

Lemmatization

What is lemmatization.

It is a process of converting a word to its base form. In other words, it is the same as stemming.

However, the main difference between stemming and lemmatization is that lemmatization considers the context and converts the word to its meaningful base form, whereas stemming just removes the last few characters, often leading to incorrect meanings and spelling errors.

For example - The word caring, stemming reduces it to “car”, where are lemmatization reduces it to “care” which is similar to the actual word.

Lemmatization can be implemented in python by using Wordnet Lemmatizer, Spacy Lemmatizer, TextBlob, Stanford CoreNLP

What are stop words?

Commonly used words like “the”, “a”, “at”, “for”, “above”, “on”, “is”, “all” are called stop words. While processing text, we delete these words as they do not provide any meaning or have a significant effect on the analysis performed. This step depends highly on the language. Python provides a library called “stopwords” that holds various pre defined stop word collections.

Part of speech tagging (POS)

What are pos tagging.

Through POS tagging, each of the tokens are assigned a part of speech(noun, verb, pronouns, adverbs etc). This is done in python using taggers like NLTK, Spacy, TextBlob, Standford CoreNLP, etc.

Named Entity Recognition

What are named entity recognition.

It is the process of detecting the named entities such as the person name, the location name, the company name, the quantities, and the monetary value.

What is Chunking?

Chunking means a grouping of words or tokens into chunks.

What is World Cloud?

A Word Cloud or Tag Cloud is a visual representation of text data in the form of tags, which are typically single words whose importance is visualized by way of their size and color. As unstructured data in the form of text continues to see unprecedented growth, especially within the field of social media, there is an ever-increasing need to analyze the massive amounts of text generated from these systems. A Word Cloud is an excellent option to help visually interpret text and is useful in quickly gaining insight into the most prominent items in a given text, by visualizing the word frequency in the text as a weighted list

Word clouds are normally used to display the frequency of appearance of words in a particular document or speech More frequently used words appear larger in the word cloud. The frequency is assumed to reflect the importance of the term in the context of the document.

Sentiment Analysis

What is sentiment analysis.

Quantifying users content, idea, belief, and opinion is known as sentiment analysis. User’s online post, blogs, tweets, feedback of product helps business people to the target audience and innovate in products and services. Sentiment analysis helps in understanding people in a better and more accurate way. It is not only limited to marketing, but it can also be utilized in politics, research, and security.

There are mainly two approaches for performing sentiment analysis.

Lexicon-based: count number of positive and negative words in given text and the larger count will be the sentiment of text.

Machine learning based approach: Develop a classification model, which is trained using the pre-labeled dataset of positive, negative, and neutral.

Term Document Matrix

What is term document matrix.

The term document matrix means we map a collection of ‘n’ documents to the vector space model by a term-document matrix. In other words, It creates a numerical representation of the documents. Representing text as a numerical structure is a common starting point for text mining and analytics such as search and ranking, creating taxonomies, categorization, document similarity, and text-based machine learning.

• An entry in the matrix corresponds to the “weight” of a term in the document; zero means the term has no significance in the document or it simply doesn’t exist in the document.
• A typical weighting is tf-idf weighting: W = tf* idf
• Term Frequency (TF): More frequent terms in a document are more important, i.e. more indicative of the topic. May want to normalize term frequency(tf) across the entire corpus: TF = (Number of times term t appears in a document) / (Total number of terms in the document)
• Inverse Document Frequency (IDF): Terms that appear in many different documents are less indicative of overall topic. IDF = log10(Total number of documents / Number of documents with term t in it)

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