The ability to identify sentiment in text, referred to as sentiment analysis, is one which is natural to adult humans. This task is, how- ever, 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 un- derstand human sociology. Here we propose two new Genetic Al- gorithms (GAs) for the task of automated text sentiment analysis. The G
As learn whether words occurring in a text corpus are ei- ther sentiment or amplifier words, and their corresponding magni- tude. Sentiment words, such as ’horrible’, add linearly to the final sentiment. Amplifier words in contrast, which are typically adjec- tives/adverbs like ’very’, multiply the sentiment of the following word. This increases, decreases or negates the sentiment of the fol- lowing word. The sentiment of the full text is then the sum of these terms. This approach grows both a sentiment and amplifier dictio- nary which can be reused for other purposes and fed into other machine learning algorithms. We report the results of multiple ex- periments 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.