7.00.02 - ExtractSentiment Arguments - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)
TextColumn
Specifies the name of the input column that contains text from which to extract sentiments.
Language
[Optional] Specifies the language of the input text:
  • 'en': English (Default)
  • 'zh_CN': Simplified Chinese
  • 'zh_TW': Traditional Chinese
Model
[Optional] Specifies the model type and file. The default model type is dictionary. If you omit this argument or specify dictionary without dict_file, then you must specify a dictionary table with alias 'dict'. If you specify both dict and dict_file, then whenever their words conflict, dict has higher priority.

The dict_file must be a text file in which each line contains only a sentiment word, a space, and the opinion score of the sentiment word.

If you specify classification model_file, then model_file must be the name of a model file generated and installed on the database by the function TrainSentimentExtractor.

Before running the function, add the location of dict_file or model_file to the user/session default search path.
Accumulate
[Optional] Specifies the names of the input columns to copy to the output table.
Level
[Optional] Specifies the level of analysis—whether to analyze each document (the default) or each sentence.
HighPriority
[Optional] Specifies the highest priority when returning results:
  • 'NEGATIVE_RECALL'

    Give highest priority to negative results, including those with lower-confidence sentiment classifications (maximizes the number of negative results returned).

  • 'NEGATIVE_PRECISION'

    Give highest priority to negative results with high-confidence sentiment classifications.

  • 'POSITIVE_RECALL'

    Give highest priority to positive results, including those with lower-confidence sentiment classifications (maximizes the number of positive results returned).

  • 'POSITIVE_PRECISION'

    Give highest priority to positive results with high-confidence sentiment classifications.

  • 'NONE'

    Give all results the same priority.

Filter
[Optional] Specifies the kind of results to return:
  • 'ALL' (Default)

    Return all results.

  • 'POSITIVE'

    Return only results with positive sentiments.

  • 'NEGATIVE'

    Return only results with negative sentiments.