Text Analysis

Twitter Sentiment Analysis: Part 2

by Kevin bonds, at 03 January 2020, category : Sentiment analysis Text analysis

Libraries Used
TF-IDF
N-Grams
Combine Skipgrams with N-grams

This is the second part of the Twitter Sentiment Analysis. I will create a TFIDF and perform some dimensionality reduction to allow me to use the mighty Random Forrest algorithm.

Twitter Sentiment Analysis: Part 1

by Kevin bonds, at 10 December 2019, category : Text analysis Sentiment analysis Feature engineering

Libraries Used
Count Features
Some Manual Work
Visualize Distributions of Engineered Features
Stratified Sampling
Tokenization
Remove Stopwords
Stemming
Create a Document-Feature Matrix
Setting up for K-fold Cross Validation
Train the First Model

The following is an analysis of the Twitter Sentiment Analysis Dataset available at: http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/. I will attempt to use this data to train a model to label unseen tweets into “Positive” or “Negative” sentiment. I will walk through my methodology and include code.

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Twitter Sentiment Analysis: Part 2

Twitter Sentiment Analysis: Part 1