Sentiment analysis using averaged weighted word vector features.

Autor: Erkan A; Computer Engineering Department, Boğaziçi University, İstanbul, Turkey., Güngör T; Computer Engineering Department, Boğaziçi University, İstanbul, Turkey.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Apr 04; Vol. 19 (4), pp. e0299264. Date of Electronic Publication: 2024 Apr 04 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0299264
Abstrakt: People use the World Wide Web heavily to share their experiences with entities such as products, services or travel destinations. Texts that provide online feedback through reviews and comments are essential for consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate the polarity of reviews. We create average review vectors from word vectors and add weights to these review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains used as standard sentiment analysis benchmarks. We ensemble the techniques with each other and existing methods, and we compare them with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Erkan, Güngör. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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