Developing a Hybrid model with Shades of Sentiment for Understanding Teenagers’ Academic Distraction Problems

Abstract

In recent years, many techniques have come up in the field of sentiment analysis. In the field of the medical domain, sentiment analysis has been used for the areas like information retrieval, feedback analysis, dialogue conversation, and review analysis. Psychological analysis through the window of sentiment analysis is still unfolded area. We have designed and developed a hybrid computational model that maps teenagers’ sentiments to their behavioral patterns and academic distraction problems. We have also developed a sentiment shaded lexicon which defines the ontology for various shades of sentiment with the help of Bling Liu’s positive and negative word dictionary. We have used a semantic lexicon-based approach and a rule-based classifier in our hybrid computational model. Rules are applied to user-input text, which teenagers write. We have extracted sentiments expressed in the user input text and we have also achieved to identify academic distraction problems of teenagers. We have computed the performance metrics of the model on 155 samples, randomly collected from teenagers (age group 13-19 years). We have computed accuracy at two stages, first at the sentiment extraction stage and second at the final output level. The error analysis is also presented in this paper. After working on error analysis, our model has achieved an accuracy of 90% for sentiment extraction and 87% for derived academic distraction problems.