A Sentiment Analysis of Men's and Women's Speech: Dictionary-based vs. Deep-learning-based Analysis
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It is well known that the language women use is different from men's use. In this paper, the researchers examine men's and women's speech through sentiment analysis. For the study of gender differences, this study uses the BNC64 corpus, whose files are selected from the British National Corpus (BNC). The BNC64 corpus is composed of 64 files, which are taken from the spoken part of the BNC. The corpus contains 32 files for male speakers and 32 files for female speakers that represent the characteristics of male vs. female differences. The study analyzed all 64 files using sentiment analysis and tried to discover the differences. It is known that there are roughly three types of approach to sentiment analysis: dictionary-based analysis, machine-learning-based analysis, and deep-learning-based analysis. This study takes the first and third kind of analyses. In the dictionary-based analysis, the researchers calculate the sentiment score (SS), sentiment word ratio (SR), and positive word ratio (PR). In the deep-learning analysis, the researchers take two different sorts of analyses: the GRU (Gated Recurrent Units) and the BERT (Bidirectional Encoder Representations from Transformers). Through the analyses, the study finds that (i) there is no significant differences in SS and SR between men and women, (ii) women usually use more positive words than men, and the differences are statistically significant, and (iii) the deep-learning-based analysis is much superior to modelling the gender differences in the sentiment analysis.
Abstract
It is well known that the language women use is different from men's use. In this paper, the researchers examine men's and women's speech through sentiment analysis. For the study of gender differences, this study uses the BNC64 corpus, whose files are selected from the British National Corpus (BNC). The BNC64 corpus is composed of 64 files, which are taken from the spoken part of the BNC. The corpus contains 32 files for male speakers and 32 files for female speakers that represent the characteristics of male vs. female differences. The study analyzed all 64 files using sentiment analysis and tried to discover the differences. It is known that there are roughly three types of approach to sentiment analysis: dictionary-based analysis, machine-learning-based analysis, and deep-learning-based analysis. This study takes the first and third kind of analyses. In the dictionary-based analysis, the researchers calculate the sentiment score (SS), sentiment word ratio (SR), and positive word ratio (PR). In the deep-learning analysis, the researchers take two different sorts of analyses: the GRU (Gated Recurrent Units) and the BERT (Bidirectional Encoder Representations from Transformers). Through the analyses, the study finds that (i) there is no significant differences in SS and SR between men and women, (ii) women usually use more positive words than men, and the differences are statistically significant, and (iii) the deep-learning-based analysis is much superior to modelling the gender differences in the sentiment analysis.
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