Sentiment Analysis on Morphologically Rich Languages: An Artificial Neural Network (ANN) Approach

Abstract

The extraction and analysis of human feelings, emotions and experiences contained in a text is commonly known as sentiment analysis and opinion mining. This research domain has several challenging tasks as well as commercial interest. The major tasks in the area of study are, identifying the subjectivity of the opinionated sentence or clause of the sentence and then classifying the opinionated text as positive or negative. In this chapter we present an investigation of machine learning approaches mainly the application of an artificial neural network (ANN) to classifying sentiments of reader reviews on news articles written in Sinhala, one of the morphologically rich languages in Asia. Sentiment analysis provides the polarity of a comment suggesting the reader’s view on a topic. We trained from a set of reader comments which were manually annotated as positive or negative and then evaluated the ANN architectures for their ability to classify new comments. The primary interest in this experiment was the exploration of selecting appropriate Adjectives and Adverbs for the classification of sentiment in a given language. The experiment was conducted in different weighting schemes by examining binary features to complex weightings for generating the polarity scores of adjectives and adverbs. We trained and evaluated several ANN architectures with supervised learning for sentiment classification. A number of problems had to be dealt with in this experiment and they are: the unavailability of the main part of speech, adjective and adverb and the sample size of the training set. Despite the issues, our approach achieved significant results for sentence level sentiment prediction in both positive and negative classification.