Docs
AI
NLP
Sentiment Classification
Optimizing for Sentiment Analysis

Optimizing for Sentiment Analysis

While standard naive Bayes text classification can work well for sentiment analysis, some small changes are generally employed that improve performance. 💪

Binary multinomial naive Bayes (binary NB)

First, for sentiment classification and a number of other text classification tasks, whether a word occurs or not seems to matter more than its frequency.

Thus it often improves performance to clip the word counts in each document at 1.

Example:

  • The example is worked without add-1 smoothing to make the differences clearer.
  • Note that the results counts need not be 1; the word great has a count of 2 even for Binary NB, because it appears in multiple documents (in both positive and negative class).
截屏2020-06-14 12.55.28

Deal with negation

During text normalization, prepend the prefix NOT_ to every word after a token of logical negation (n’t, not, no, never) until the next punc- tuation mark.

Example:

截屏2020-06-14 12.58.03

Deal with insufficient labeled training data

Derive the positive and negative word features from sentiment lexicons, lists of words that are pre-annotated with positive or negative sentiment.

Popular lexicons:

  • General Inquirer
  • LIWC
  • opinion lexicon
  • MPQA Subjectivity Lexicon