LexiPers: An ontology based sentiment lexicon for Persian

TL;DR

This paper presents LexiPers, a new ontology-based sentiment lexicon for Persian, generated using a graph-based seed expansion and classification methods, achieving promising accuracy and F-measure.

Contribution

It introduces a novel graph-based seed selection and expansion method for creating a Persian sentiment lexicon using ontology and classification techniques.

Findings

The generated lexicon shows acceptable accuracy.

The F-measure indicates reliable sentiment classification.

The approach effectively leverages ontology for lexicon expansion.

Abstract

Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-nearest neighbors and nearest centroid methods for classification. These classifiers have been evaluated based on a set of hand labeled synsets. The final sentiment lexicon has been generated by the best classifier. The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.