Refining Word Embeddings Based on Improved Genetic Algorithm for Sentiment Analysis
| Autor: | Yongquan Liang, Jianyan Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Sentiment analysis Treebank Context (language use) 02 engineering and technology computer.software_genre Semantics Lexicon Ranking (information retrieval) Binary classification 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Word (computer architecture) Natural language processing |
| Zdroj: | 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). |
| DOI: | 10.1109/itaic49862.2020.9339058 |
| Popis: | Word embeddings have been extensively used for sentiment analysis tasks. However, typical existing algorithms only model the syntactic context of words but fail to capture sufficient sentiment information of text, which affects the performance of sentiment analysis. Therefore, this paper presents an word vector refinement model based on improved genetic algorithm, which uses sentiment lexicon to obtain the sentiment ranking of the semantic nearest neighbors of the target word, and uses an improved genetic algorithm to optimize the vector representations of words such that they get the sentiment information of the word. Experimental results show that the proposed model can improve conventional word embeddings for binary classification on Internet Movie Database (IMDB) and fine-grained classification on Stanford Sentiment Treebank (SST). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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