Abstract: An input text that is also transmitted to a text processing service (e.g., a cloud based text processing service) is received. Characterizing information (e.g., contiguous parts of speech, terms used per part of speech, payload length, etc.) is extracted from the input text. A text payload is generated using the characterizing information. A performance test is run on the text payload. The performance test can include performing at least one selected from a group consisting of: sentiment analysis on the text payload, entity analysis on the text payload, content classification on the text payload, and syntax analysis on the text payload. The performance test can yield a processing time required to perform the performance test. Memory and processing power resource allocation to the text processing service can be altered based on the processing time of the performance test.

Abstract: Provided are a computer program product, system, and method for interpreting text classification predictions through deterministic extraction of prominent n-grams. A determination is made of n-gram vectors comprising word embeddings of n-grams in a document and of a document vector comprising word embeddings of the document. A label is received from the text classifier program, comprising a text classification of the document. A determination is made of a label vector comprising word embeddings of the label. The n-gram vectors, the document vector, and the label vector are used to determine n-grams that explain the text classification of the text classifier program.

Abstract: An input text that is also transmitted to a text processing service is received. Characterizing information is extracted from the input text. A text payload is generated using the characterizing information. A performance test is run on the text payload.

Abstract: Provided are a computer program product, system, and method for interpreting text classification predictions through deterministic extraction of prominent n-grams. A determination is made of n-gram vectors comprising word embeddings of n-grams in a document and of a document vector comprising word embeddings of the document. A label is received from the text classifier program, comprising a text classification of the document. A determination is made of a label vector comprising word embeddings of the label. The n-gram vectors, the document vector, and the label vector are used to determine n-grams that explain the text classification of the text classifier program.