The linguistic construction of sentiment expressions in student opinionated content: A corpus-based study

Abstract

AbstractMotivated by an increasing use of social media for the expression of personal stance towards a certain target, we analyse the language used to produce such opinionated content with expressions of sentiment, which represents the main data source for sentiment analysis. We use the first manually annotated corpus for sentiment analysis of the Serbian language developed for the service sector of higher education. Our study focuses on how various linguistic constructions, used in different context, influence the sentiment polarity of a text. Our findings indicate that sentiment expressions and negation have a most significant role in determining whether the text conveys positive, neutral, or negative sentiment, while intensifiers (words which either increase or decrease sentiment) have a considerable influence on sentiment intensity. We also present an analysis of the impact of conjunctions, conditional sentences, comparative and modal verbs, and pronouns on sentiment polarity and intensity. Based on the derived observations, we propose a set of rules that could be integrated with machine learning algorithms into an automated sentiment analysis system for the Serbian language. Our findings also make a much-needed contribution to the few currently available resources for natural language processing of Serbian.