Resource Creation and its Evaluation for Aspect Based Sentiment Analysis in Telugu

Abstract

In recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the Internet. It has various applications like opinion mining, social media monitoring, and market research. Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sentiment expressed. Significant contributions have been made in ABSA, but this progress is limited only to a few languages with adequate resources. Telugu lags behind in this area of research despite being one of the most spoken languages in India and an enormous amount of data being created each day. In this thesis, we create a reliable resource for aspect based sentiment analysis in Telugu. The data is annotated for three tasks namely Aspect Term Extraction, Aspect Polarity Classification and Aspect Categorisation. Further, we develop baselines for the tasks using deep learning methods demonstrating the reliability and usefulness of the resource. In addition to this, experimentation has been done with transformer models, as they have led to pivotal changes in the field of NLP. Specifically, the setting of monolingual and multilingual models has been chosen to compare the effective contribution of the dataset created for ABSA in Telugu.