  Web 2.0 services have enabled people to express their opinions, experience
and feelings in the form of user-generated content. Sentiment analysis or
opinion mining involves identifying, classifying and aggregating opinions as
per their positive or negative polarity. This paper investigates the efficacy
of different implementations of Self-Organizing Maps (SOM) for sentiment based
visualization and classification of online reviews. Specifically, this paper
implements the SOM algorithm for both supervised and unsupervised learning from
text documents. The unsupervised SOM algorithm is implemented for sentiment
based visualization and classification tasks. For supervised sentiment
analysis, a competitive learning algorithm known as Learning Vector
Quantization is used. Both algorithms are also compared with their respective
multi-pass implementations where a quick rough ordering pass is followed by a
fine tuning pass. The experimental results on the online movie review data set
show that SOMs are well suited for sentiment based classification and sentiment
polarity visualization.
