Social Media Analysis
The goal of text classification is to automatically classify the text documents into one or more defined categories. In this tutorial, the author will explain about the text classification and the step by step processing to implement it in python.
This recipe will compare two machine learning approaches to see which is more likely to give an accurate analysis of sentiment. Both approaches analyse a corpora of positive and negative Movie Review data by training and thereafter testing to get an accuracy score. The techniques are Support Vector Machines (SVM) and Naive Bayes.
This recipe is part of the Text Analysis for Twitter Research (TATR) series, and will look at tokenizing and extracting key features from a Tweet.
This recipe is part of the Text Analysis for Twitter Research (TATR) series. This recipe will describe Panda dataframe manipulation, in particular the techniques used for some of the more advanced Twitter analysis found in the TATR library.
This recipe is part of the Text Analysis for Twitter Research (TATR) series. The recipe will show how to load and save a CSV (comma-separated values) file into a Panda data structure.
This recipe is part of the Text Analysis for Twitter Research (TATR) series and describes how to begin plotting basic graphs using Twitter data.
This recipe is part of the Text Analysis for Twitter Research (TATR) series. The recipe will look at categorizing text using the General Inquirer Categories released by Harvard.
This recipe is part of the Text Analysis for Twitter Research (TATR) series. In this recipe we will show you how to use a dataset of Tweets to find the most popular hashtags by date. The results can then be manipulated by placing them in a Panda dataframe and visualized by plotting the most popular hashtag points over time.
In this recipe we use 3 ebooks to show how topic analysis can identify the different topics each text represents. We will use Latent Dirichlet Allocation (LDA) approach which is the most common modelling method to discover topics. We can then spice it up with an interactive visualization of the discovered themes. This recipe is based on Zhang Jinman's notebook found on TAPoR.
NB: Any number of texts can be used, we choose 3 for this recipe.
This recipe shows how to scrape comments from a YouTube video to analyze.