CreateSemanticNetwork: Note: this function is DEPRECATED and will be removed in a future release.

Description

Create semantic networks from social media data (semantic relationships between concepts)

Usage

CreateSemanticNetwork(x, writeToFile, termFreq, hashtagFreq,
  removeTermsOrHashtags, stopwordsEnglish)

Arguments

a data frame of class dataSource. For Twitter data, it is also possible to provide a *list* of data frames (i.e. data frames that inherit class dataSource and twitter). Only lists of Twitter data frames are supported at this time. If a list of data frames is provided, then the function binds these row-wise and computes over the entire data set.

writeToFile

logical. If TRUE then the network is saved to file in current working directory (GRAPHML format), with filename denoting the current date/time and the type of network.

termFreq

numeric integer, specifying the percentage of most frequent TERMS to include. For example, a value of 20 means that the 20 percent most frequently occurring terms will be included in the semantic network. The default value is 5, meaning the 5 percent most frequent terms are used.

hashtagFreq

** NOT IMPLEMENTED YET - DEFAULTS TO ALL HASHTAGS **. numeric integer, specifying the percentage of most frequent HASHTAGS to include. For example, a value of 80 means that the 80 percent most frequently occurring hashtags will be included in the semantic network. The default value is 50, meaning the 50 percent most frequent hashtags are used.

removeTermsOrHashtags

character vector. Default is none. Otherwise this argument specifies which terms or hashtags (i.e. vertices with matching `name`) should be removed from the semantic network. This is useful to remove the search term or hashtag that was used to collect the data (i.e. remove the corresponding vertex in the graph). For example, a value of "#auspol" means that if there is a vertex with the name "#auspol" then this vertex will be removed.

stopwordsEnglish

logical. If TRUE then English stopwords are removed from the tweets (e.g. words such as 'the' or 'and'). Using FALSE may be helpful non-English data sets. The default is TRUE (i.e. stopwords will be removed).

Value

An igraph graph object, with weighted edges.

Details

This function creates a semantic network from social media data (i.e. from data frames of class dataSource, or for Twitter data it is also possible to provide a list of data frames). In such semantic networks, concepts are words/terms extracted from the text corpus of social media data (e.g. tweets on Twitter).

This function creates a weighted network from a data frame of class dataSource (which are created using the `CollectData` family of functions in the SocialMediaLab package), or a list of Twitter data frames collected using CollectDataTwitter function.

The resulting semantic network is an igraph graph object. This graph object is semantic because vertices represent unique concepts (in this case unique terms/words extracted from a social media text corpus), and edges represent the co-occurrence of terms for all observations in the data set. For example, for a Twitter semantic network, vertices represent either hashtags (e.g. "#auspol") or single terms ("politics"). If there are 1500 tweets in the data set (i.e. 1500 observations), and the term "#auspol" and the term "politics" appear together in every tweet, then this will be represented by an edge with weight equal to 1500.

Examples

Run this code


## Not run: ------------------------------------
#   ## This example shows how to collect Twitter data and create a semantic network
# 
#   # Firstly specify your API credentials
#   my_api_key <- "1234567890qwerty"
#   my_api_secret <- "1234567890qwerty"
#   my_access_token <- "1234567890qwerty"
#   my_access_token_secret <- "1234567890qwerty"
# 
#   # Authenticate with the Twitter API using \code{AuthenticateWithTwitterAPI}
#   AuthenticateWithTwitterAPI(api_key=my_api_key, api_secret=my_api_secret,
#     access_token=my_access_token, access_token_secret=my_access_token_secret)
# 
#   # Collect tweets data using \code{myTwitterData}
#   myTwitterData <- CollectDataTwitter(searchTerm="#auspol",
#     numTweets=200,writeToFile=FALSE,verbose=FALSE)
# 
#   # Create a 'semantic' network using \code{CreateSemanticNetwork}
#   g_semantic_twitter <- CreateSemanticNetwork(myTwitterData,writeToFile=FALSE,
#     termFreq=20,hashtagFreq=80)
## ---------------------------------------------