Inpirical-Coder

# Short scripts for testing three different sentiment classifiers on tweets,
# acquiring the tweets used for testing,
# calculating systems' precision, recall and F-measures.


require(RCurl)   # For downloading file from a given URL.
require(twitteR) # Used for the 'twitter' class.
require(sentiment) # For bayes and voter classifiers.
source("sent140.R")  # Used for the Sentiment 140 API. Can be downloaded from here:
# https://github.com/okugami79/sentiment140/blob/master/R/sentiment.r

Inpirical-Coder

# Simple script for doing some data-analysis of tweets;
# looking at "sentiment" and "emotion" using the sentiment package.
# see https://sites.google.com/site/miningtwitter/questions/sentiment/sentiment
# for background.

# SETTINGS
# =============================================================================
authenticated    = TRUE # If TRUE will load credential from file.
tweets.from.file = TRUE # If TRUE will load tweets from file rather than query.
no.tweets        = 1500    # Number of tweets to fetch in every search; <= 1,500.

Inpirical-Coder

# Code to fetch news streams from 5 live sources, process the streams and text
# and apply a simple sentiment scoring algorigthm.
#
# A writeup of the analysis can be found here:
# https://www.linkedin.com/pulse/article/20141109035942-34768479-r-sentiment-scoring-hsbc-w-harvard-general-inquirer

# Define the packages we want to load:
packs = c(
  "tm",                         # Text mining
  "tm.plugin.webmining",        # Web-source plugin for text mining

Inpirical-Coder

# Some code to asses an SVM with a two-dimensional feature space:
# trend (price - simple moving average) and relative strength index.
# The code is an adaptation of the code found in the following linkedin post:
# "Trading the RSI using a Support Vector Machine"
# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

# Settings
sma.window = 50 # Number of observations in simple moving average.
rsi.window = 3  # Number of observation in relative strength index (RSI)

Inpirical-Coder

# This is a simple example for acquiring text-stream assets using the R "tm" package

require(tm)                     # Load the text-mining package.
require(tm.plugin.webmining)    # Web-mining plugin for text mining.
require(SnowballC)              # Package for stemming.

# Define the symbol we want to acquire news on.
sym = "NYSE:HSBC"

# Build a corpus of the news items.