Using text features

DSS provides several builtin ways to handle text features, such as Counts vectorization (See Text variables for more details).

However, for Deep Learning algorithms, you may want to use a Custom preprocessing to build 2-D or 3-D vectors . In that case, you need to write your own processor (See Custom Preprocessing). You can use the TokenizerProcessor provided by DSS:

from import TokenizerProcessor

# Defines a processor that tokenizes a text. It computes a vocabulary on all the corpus.
# Then, each text is converted to a vector representing the sequence of words, where each
# element represents the index of the corresponding word in the vocabulary. The result is
# padded with 0 up to the `max_len` in order for all the vectors to have the same length.

#   num_words  - maximum number of words in the vocabulary
#   max_len    - length of each sequence. If the text is longer,
#                it will be truncated, and if it is shorter, it will be padded
#                with 0.
processor = TokenizerProcessor(num_words=10000, max_len=32)

With this example, the output for each text will have a (32) shape. Then, this output is sent to a textFeature_preprocessed input, so the corresponding input in the model should look like:

input_text = Input(shape=(32), name="textFeature_preprocessed")

See Deep learning for sentiment (text) analysis for a step-by-step example of this in practice.