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Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis

Su Yang Orcid Logo, Farzin Deravi

Applied Sciences, Volume: 12, Issue: 18, Start page: 9287

Swansea University Author: Su Yang Orcid Logo

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DOI (Published version): 10.3390/app12189287

Abstract

In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimizing the feature vectors explicitly. Engineering fe...

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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61289
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Abstract: In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimizing the feature vectors explicitly. Engineering feature vectors to match the data between the training set and query sample as proposed in this paper could be a promising way for boosting the classification performance in machine learning applications. The proposed mechanism is designed to re-engineer the feature components from a set of embedding vectors for greatly increased between-class separation, hence better leveraging the informative content of the documents. The proposed mechanism was evaluated using four public benchmarking datasets for both two-way and five-way semantic classifications. The resulting embeddings have demonstrated substantially improved performance for a range of sentiment analysis tasks. Tests using all the four datasets achieved by far the best classification results compared with the state-of-the-art.
Keywords: sentiment analysis; semantic classification; feature re-engineering; NLP
College: College of Science
Funders: Swansea University
Issue: 18
Start Page: 9287