Abstract:
Sentiment Analysis (SA) is an application of Natural Language Processing (NLP)
to analyse the sentiments expressed in the text. It classifies into categories of
qualities and opinions such as good, bad, positive, negative, neutral, etc. It
employs machine learning techniques and lexicons for the classification.
Nowadays, people share their opinions or feelings about movies, products,
services, etc. through social media and online review sites. Analysing their
opinions is beneficial to the public, business organisations, film producers and
others to make decisions and improvements. SA is mostly employed in English
language but rare for Indian languages including Tamil. This review paper aims
to critically analyse the recent literature in the field of SA with Tamil text.
Objectives, Methodologies and success rates are taken in consideration for the
review. We shall conclude from the review that SVM and RNN classifiers taking
TF-IDF and Word2vec features of Tamil text give better performance than
grammar rules based classifications and other classifiers with presence of words,
TF and BoW as features.
