  The ability to identify sentiment in text, referred to as sentiment analysis,
is one which is natural to adult humans. This task is, however, not one which a
computer can perform by default. Identifying sentiments in an automated,
algorithmic manner will be a useful capability for business and research in
their search to understand what consumers think about their products or
services and to understand human sociology. Here we propose two new Genetic
Algorithms (GAs) for the task of automated text sentiment analysis. The GAs
learn whether words occurring in a text corpus are either sentiment or
amplifier words, and their corresponding magnitude. Sentiment words, such as
'horrible', add linearly to the final sentiment. Amplifier words in contrast,
which are typically adjectives/adverbs like 'very', multiply the sentiment of
the following word. This increases, decreases or negates the sentiment of the
following word. The sentiment of the full text is then the sum of these terms.
This approach grows both a sentiment and amplifier dictionary which can be
reused for other purposes and fed into other machine learning algorithms. We
report the results of multiple experiments conducted on large Amazon data sets.
The results reveal that our proposed approach was able to outperform several
public and/or commercial sentiment analysis algorithms.
