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18-07-2022

Automatically Constructing a Fine-Grained Sentiment Lexicon for Sentiment Analysis

Authors: Yabing Wang, Guimin Huang, Maolin Li, Yiqun Li, Xiaowei Zhang, Hui Li

Published in: Cognitive Computation | Issue 1/2023

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Abstract

Sentiment analysis is an important research area in natural language processing (NLP), and the performance of sentiment analysis models is largely influenced by the quality of sentiment lexicons. Existing sentiment lexicons contain only the sentiment information of words. In this paper, we propose an approach for automatically constructing a fine-grained sentiment lexicon that contains both emotion information and sentiment information to solve the problem that the emotion and sentiment of texts cannot be jointly analyzed. We design an emotion-sentiment transfer method and construct a fine-grained sentiment seed lexicon, and we then expand the sentiment seed lexicon by applying the graph dissemination method to the synonym set. Subsequently, we propose a multi-information fusion method based on neural network to expand the sentiment lexicon based on a corpus. Finally, we generate the Fine-Grained Sentiment Lexicon (FGSL), which contains 40,554 words. FGSL achieves F1 values of 61.97%, 69.58%, and 66.99% on three emotion datasets and 88.19%, 89.31%, and 86.88% on three sentiment datasets. Experimental results on multiple public benchmark datasets illustrate that FGSL achieves significantly better performance in both emotion analysis and sentiment analysis tasks.

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Metadata
Title
Automatically Constructing a Fine-Grained Sentiment Lexicon for Sentiment Analysis
Authors
Yabing Wang
Guimin Huang
Maolin Li
Yiqun Li
Xiaowei Zhang
Hui Li
Publication date
18-07-2022
Publisher
Springer US
Published in
Cognitive Computation / Issue 1/2023
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10043-1

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