Sentiment analysis using averaged weighted word vector features.

ABSTRACT: 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 use

SUBMITTER: Erkan A 

PROVIDER: S-EPMC10994307 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature