Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 
  In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>),
  it also allows the user to create embedding vectors for text which can be used in other analyses.
  GPU acceleration is supported on Windows and Linux.

Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.

Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).

Implements parsimonious mixtures of MSEN and MTIN distributions via expectation-
    maximization based algorithms for model-based clustering. For each mixture
    component, parsimony is reached via the eigen-decomposition of the scale 
    matrices and by imposing a constraint on the tailedness parameter. This produces
    a family of 28 parsimonious mixture models for each distribution.

Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.

A framework that joins topic modeling and sentiment analysis of
  textual data. The package implements a fast Gibbs sampling estimation of
  Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
  <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
  Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
  helpers and visualizations to analyze the result of topic modeling. The
  framework also allows enriching topic models with dates and externally
  computed sentiment measures. A flexible aggregation scheme enables the
  creation of time series of sentiment or topical proportions from the enriched
  topic models. Moreover, a novel method jointly aggregates topic proportions
  and sentiment measures to derive time series of topical sentiment.