LexiPers: An ontology based sentiment lexicon for Persian

11 pages•Published: September 29, 2016

Behnam Sabeti, Pedram Hosseini, Gholamreza Ghassem-Sani and Sَeyed Abolghasem Mirroshandel

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.

Keyphrases: document classification, ontology, persian, sentiment analysis, sentiment lexicon

In: Christoph Benzmüller, Geoff Sutcliffe and Raul Rojas (editors). GCAI 2016. 2nd Global Conference on Artificial Intelligence, vol 41, pages 329-339.

Links:	https://easychair.org/publications/paper/q2F
	https://doi.org/10.29007/f4j4

BibTeX entry

@inproceedings{GCAI2016:LexiPers_ontology_based_sentiment,
  author    = {Behnam Sabeti and Pedram Hosseini and Gholamreza Ghassem-Sani and Sَeyed Abolghasem Mirroshandel},
  title     = {LexiPers: An ontology based sentiment lexicon for Persian},
  booktitle = {GCAI 2016. 2nd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Geoff Sutcliffe and Raul Rojas},
  series    = {EPiC Series in Computing},
  volume    = {41},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/q2F},
  doi       = {10.29007/f4j4},
  pages     = {329-339},
  year      = {2016}}

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