Aspect based Nepali Text Sentiment Analysis using BiLSTM

Abstract

Aspect-Based Sentiment Analysis is a text analysis technique that classifies the sentiment of a specific aspect in a text. With a rise in internet and social media access, people tend to decide to buy a product or to visit a restaurant based on comments and reviews. Categorical reviews can play a significant role in improving the business of a restaurant if a business entity can capture insights of those reviews and use productively. Since Nepali is a low resource language there is no significant numbers of the dataset for restaurant reviews. Therefore a dataset is created in the work to model a target aspect-based sentiment analysis in restaurant review domain. The dataset comprises of comments extracted from social media sites, websites, and manually prepared data. Data are cleaned, pre-processed then aspects and sentiments are extracted using POS tagging. Sentiments and target categories are classified using word embedding and the deep learning BiLSTM model. The sentiment classification model classifies the sentiments into positive or negative. The target classification model classifies text into 5 target categories Food (F), Price (P), Service (S), Hygiene (H), and Experience (E). The result was found to be good compared to similar models in other domains. The sentiment prediction model and target category prediction model attains the F1 Score of 0.88 and 0.76 respectively.