Text Mining and Analytics: Hotel Reviews Sentiment Analysis

Text Mining and Analytics: Hotel Reviews Sentiment Analysis

• discover the key concepts covered in this course
• demonstrate how to load the hotel review data and recognize its features
• install and import the required libraries and demonstrate data loading
• utilize Exploratory Data Analysis (EDA), topic modelling, sentiment analysis, and preprocessing of data
• demonstrate the use of WordCLoud and sentiment distribution
• build and evaluate simple NLP models
• interpret the tuning of models for better results and outline their evaluation using different search methods
• deploy AutoML, PyCaret, and Streamlit models
• identify best practices for NLP projects across various industries
• compare challenges and deployment strategies for NLP projects across various industries
• summarize the key concepts covered in this course

Using natural language processing (NLP) tools, an organization can analyze their review data and predict the sentiments of their customers.

In this course, we'll learn how to implement NLP tools to solve a business problem end-to-end. To begin, learn about loading, exploring, and preprocessing business data. Next, explore various linguistic features and feature engineering methods for data and practice building machine learning (ML) models for sentiment prediction. Finally, examine the automation options available for building and deploying models.

After completing this course, you will be able to solve NLP problems for enterprises end-to-end by leveraging a variety of concepts and tools.

Text Mining and Analytics: Machine Learning for Natural Language Processing

Text Mining and Analytics: Machine Learning for Natural Language Processing

• discover the key concepts covered in this course
• recognize key concepts of NLP with ML
• describe end-to-end components for NLP problems
• illustrate the use of one-hot encoding, bag-of-words, n-gram, and TFIDF
• restate logistic regression, support vector machine (SVM), Naive Bayes, and boosting models
• demonstrate data loading and a basic overview of columns
• perform Exploratory Data Analysis (EDA) of data
• perform an exploration of linguistic features in data
• demonstrate feature engineering on data
• demonstrate simple model building and evaluation using the Decision Tree classifier, logistic regression, and SVM
• demonstrate simple model building and evaluation using the Random Forest Classifier, Naïve Bayes, and KNN and compare the results of all the models
• perform model tuning for better results and evaluation using different search methods
• summarize the key concepts covered in this course

Machine learning (ML) is one of the most important toolsets available in the enterprise world. It gives predictive powers to data that can be leveraged to investigate future behaviors and patterns. It can help companies proactively improve their business and help optimize their revenue. Learn how to leverage machine learning to make predictions with language data. Explore the ML pipelines and common models used for Natural Language Processing (NLP). Examine a real-world use case of identifying sarcasm in text and discover the machine learning techniques suitable for NLP problems. Learn different vectorization and feature engineering methods for text data, exploratory data analysis for text, model building, and evaluation for predicting from text data and how to tune those models to achieve better results. After completing this course, you'll be able to illustrate the use of machine learning to solve NLP problems and demonstrate the use of NLP feature engineering.

Text Mining and Analytics: Natural Language Processing Libraries

Text Mining and Analytics: Natural Language Processing Libraries

• discover the key concepts covered in this course
• recognize polyglot and TextBlob and outline the benefits of these options over Natural Language Toolkit (NLTK) and spaCy with use cases
• explain the existence of Gensim and CoreNLP and describe the benefits of these options over NLTK and spaCy with use cases
• install linguistic features including Named Entity Recognition (NER), part of speech (POS) tagging, morphological analysis, and multiple languages support
• demonstrate multi-language part of speech tagging and morphological analysis using PolyGlot
• demonstrate multi-language parts of speech tagging using polyglot including language detection, sentiment analysis, and transliteration
• install linguistic features including noun phrase extraction, POS, parsing, and WordNet integration
• demonstrate additional features of TextBlob including sentiment analysis, classification models, tokenization, word/phrase frequencies, word inflection, and spelling correction
• demonstrate installation and topic modeling with Gensim
• demonstrate building bigram and trigram for topic modeling using Genism
• demonstrate building an LDA model for topic modeling using genism
• demonstrate advanced Genism features such as identifying query similarity
• summarize the key concepts covered in this course

Text Mining and Analytics: Pattern Matching & Information Extraction

Text Mining and Analytics: Pattern Matching & Information Extraction

• discover the key concepts covered in this course
• outline the heuristic approach for natural language processing (NLP)
• recall why WordNet is important
• illustrate and extract synonyms and identify WordNet hierarchies - hypernyms and hyponyms
• identify meronyms and holonyms
• demonstrate the lexical resource for opinion mining and finding the sentiment of text
• demonstrate the Python RE Module, RE - Search, Find ALL, Finditer, Groups, Find and Replace, and Split
• demonstrate anchors, character classes, greedy, lazy and backtracking algorithms, and performance
• perform basic information extraction using NLTK chunking and regex rules
• perform advanced information extraction using NLTK chunking and regex rules
• model and find sentiment of movie plots using SentiWordNet
• summarize the key concepts covered in this course