Predict polarity for the given dataset.

Predict polarity for the given dataset and filters using the pre-trained Keras LSTM model.

Arguments

data	the sentiment140 test text for text of the tweet.
user_list	a vector of users for which to filter the dataset
start_date_time	input start_date_time in POSIXct format on which to filter the dataset
end_date_time	input end_date_time in POSIXct format on which to filter the dataset
keyword_list	a list of string keywords on which to filter the dataset
maxlen	Maximum length of a sequence.
max_words	Maximum number of words to consider using word frequency measure.
trained_data	Training dataset.
model_load_path	File path location of the trained model file.

Value

a list object with raw filtered dataframe, predictions dataframe that holds the predicted polarity using the model, confusion_matrix, multiple model performance statistics and a plot comparing the actual and predicted polarity.

Examples

library(deepSentimentR)
predict_polarity_keras()
#> $raw
#> # A tibble: 354 x 14
#>    polarity    id date                query user  text  nouns adjectives
#>    <chr>    <int> <dttm>              <chr> <chr> <chr> <int>      <int>
#>  1 Positive   217 2009-05-25 17:29:39 mcdo… Mami… mgg …     7          2
#>  2 Positive  2140 2009-05-20 02:38:17 nike  Chet… ew n…     4          2
#>  3 Negative   224 2009-05-25 17:34:51 chen… QCWo… ife?…     9          3
#>  4 Positive   569 2009-06-07 21:38:16 kind… rach… @lon…     8          2
#>  5 Positive  2546 2009-06-08 00:13:48 kind… k8tb… " lo…     7          1
#>  6 Positive  1019 2009-05-11 05:21:25 lebr… unde… atch…     3          1
#>  7 Negative  2110 2009-05-18 01:14:35 Malc… blin… @por…     7          3
#>  8 Positive   256 2009-05-27 23:59:18 goog… maex… " am…     3          1
#>  9 Negative   413 2009-06-02 03:17:04 time… Jaso… " ha…    11          4
#> 10 Positive  1003 2009-05-11 03:18:59 kind… Happ… y Ki…     1          0
#> # … with 344 more rows, and 6 more variables: prepositions <int>,
#> #   articles <int>, pronouns <int>, verbs <int>, adverbs <int>,
#> #   interjections <int>
#> 
#> $predictions
#> # A tibble: 354 x 15
#>    polarity    id date                query user  text  nouns adjectives
#>       <dbl> <int> <dttm>              <chr> <chr> <chr> <int>      <int>
#>  1        1   217 2009-05-25 17:29:39 mcdo… Mami… mgg …     7          2
#>  2        1  2140 2009-05-20 02:38:17 nike  Chet… ew n…     4          2
#>  3        0   224 2009-05-25 17:34:51 chen… QCWo… ife?…     9          3
#>  4        1   569 2009-06-07 21:38:16 kind… rach… @lon…     8          2
#>  5        1  2546 2009-06-08 00:13:48 kind… k8tb… " lo…     7          1
#>  6        1  1019 2009-05-11 05:21:25 lebr… unde… atch…     3          1
#>  7        0  2110 2009-05-18 01:14:35 Malc… blin… @por…     7          3
#>  8        1   256 2009-05-27 23:59:18 goog… maex… " am…     3          1
#>  9        0   413 2009-06-02 03:17:04 time… Jaso… " ha…    11          4
#> 10        1  1003 2009-05-11 03:18:59 kind… Happ… y Ki…     1          0
#> # … with 344 more rows, and 7 more variables: prepositions <int>,
#> #   articles <int>, pronouns <int>, verbs <int>, adverbs <int>,
#> #   interjections <int>, pred_polarity[,1] <int>
#> 
#> $confusion_matrix
#> # A tibble: 4 x 3
#>   polarity pred_polarity count
#>   <fct>    <fct>         <int>
#> 1 0        0               135
#> 2 0        1                40
#> 3 1        0                34
#> 4 1        1               145
#> 
#> $true_negative
#> [1] 135
#> 
#> $true_positive
#> [1] 145
#> 
#> $false_positive
#> [1] 40
#> 
#> $false_negative
#> [1] 34
#> 
#> $precision
#> [1] 0.7837838
#> 
#> $recall
#> [1] 0.8100559
#> 
#> $f1
#> [1] 0.7967033
#> 
#> $accuracy
#> [1] 0.7909605
#> 
#> $plot
#>