tv_sentiment_index_all_coefs: TV sentiment index using all positive and negative...

In lucasgodeiro/TextForecast: Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary

Description

TV sentiment index using all positive and negative coefficients.

Usage

tv_sentiment_index_all_coefs(x, w, y, alpha, lambda, newx, family, scaled,
  k_mov_avg, type_mov_avg)

Arguments

x	A matrix of variables to be selected by shrinkrage methods.
w	Optional Argument. A matrix of variables to be selected by shrinkrage methods.
y	the response variable.
alpha	the alpha required in glmnet.
lambda	the lambda required in glmnet.
newx	Matrix that selection will be applied. Useful for time series, when we need the observation at time t.
family	the glmnet family.
scaled	Set TRUE for scale and FALSE for no scale.
k_mov_avg	The moving average order.
type_mov_avg	The type of moving average. See movavg.

Value

A list with the net, postive and negative sentiment index. The net time-varying sentiment index. The index is based on the word/term counting and is computed using: tv_index=(pos-neg)/(pos+neg). The postive sentiment index is computed using: tv_index_pos=pos/(pos+neg) and the negative tv_index_neg=neg/(pos+neg).

Examples

suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
data("stock_data")
data("news_data")
y=as.matrix(stock_data[,2])
w=as.matrix(stock_data[,3])
data("news_data")
X=news_data[,2:ncol(news_data)]
x=as.matrix(X)
grid_alphas=0.05
cont_folds=TRUE
t=length(y)
optimal_alphas=optimal_alphas(x=x[1:(t-1),],
                              y=y[2:t],grid_alphas=grid_alphas,cont_folds=TRUE,family="gaussian")
tv_idx=tv_sentiment_index_all_coefs(x=x[1:(t-1),],y=y[2:t],alpha = optimal_alphas[1],
                                 lambda = optimal_alphas[2],newx=x,
                                 scaled = TRUE,k_mov_avg = 4,type_mov_avg = "s")

lucasgodeiro/TextForecast documentation built on Sept. 19, 2019, 3:41 a.m.