TextForecast (version 0.1.3)

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

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

# NOT RUN {
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")
# }