Work

Improving Dutch sentiment analysis in Pattern

Year: 2020

Type: article

Abstract: In this paper we investigate methods for improving the sentiment analysis functionality of Pattern.nl, the Dutch submodule of Pattern, an open-source library for web mining and natural language processing. We discuss the impact on performance of three different potential improvements: extending the module’s internal sentiment lexicon; removing subsets of neutral words from the sentiment lexicon; and improving the algorithm for combining multiple word-level sentiment ratings into a sentence-level sentiment rating. We evaluated the improvements on datasets from the product review domain (books, clothing and music) and a dataset of short emotional stories. The experiments show that lexicon expansion does not lead to better results; new normalization techniques, on the other hand, show a limited but consistent performance increase for sentiment ratings.

Source: Computational Linguistics in the Netherlands Journal

Authors: Lorenzo Gatti, Judith van Stegeren

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: University of Twente, Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Awards: Data2Game: Enhanced Efficacy of Computerised Training via Player Modelling and Individually Tailored Scenarios, Producing Affective Language: Content Selection, Message Formulation And Computational Modelling

OpenAlex ID: W3127577368

FWCI: 0.084

Cites: 12

Cited by: 1

Related to: 10