Communication Methods and Measures· 2018Q1
More than Bags of Words: Sentiment Analysis with Word Embeddings
- 223citations
- Q1SCImago
- 2018year
Short summary
A new sentiment analysis method using word embeddings, which capture word meaning similarities, achieved high accuracy in estimating negativity in parliamentary speeches.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces a sentiment analysis method based on word embeddings, capturing word meaning similarities.
- The method uses word embeddings within a supervised machine learning procedure to estimate negativity.
- Accuracy was evaluated with crowd-coded training sentences.
- External validity was tested on Austrian parliamentary speeches.
AI-generated from the title and abstract; the full text is not read.
Abstract
Moving beyond the dominant bag-of-words approach to sentiment analysis we introduce an alternative procedure based on distributed word embeddings. The strength of word embeddings is the ability to capture similarities in word meaning. We use word embeddings as part of a supervised machine learning procedure which estimates levels of negativity in parliamentary speeches. The procedure’s accuracy is evaluated with crowdcoded training sentences; its external validity through a study of patterns of negativity in Austrian parliamentary speeches. The results show the potential of the word embeddings approach for sentiment analysis in the social sciences.
The authors' abstract, as published at the source. Communication Methods and Measures, 2018 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences