Sentiment analysis with genetic programming

Abstract

With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to the surge of the sentiment analysis (SA) field. One of the most studied subfields of SA is polarity detection, which is the problem of classifying a text as positive, negative, or neutral. This classification problem is difficult to solve automatically, and many hand-adjusted resources are needed to overcome the difficulties in detecting sentiment from text. These resources include hand-adjusted textual features as well as lexicons. Deciding which resource and which combination of resources are more appropriate to a given scenario is a time-consuming trial-and-error process. Thus, in this work, we propose the use of Genetic Programming (GP) as a tool for automatically choosing, combining, and classifying sentiment from text. We propose a series of functions that allow GP to deal with preprocessing tasks, handcrafted features, and automatic weighting of lexicons for a given training set. Our experiments show that our GP solution is competitive and sometimes better than SVM and superior to naïve Bayes, logistic regression, and stochastic gradient descent, which are methods used in SA competitions.