Comprehending Lexical and Affective Ontologies in the Demographically
  Diverse Spatial Social Media Discourse
Salim Sazzed
This study aims to comprehend linguistic and socio-demographic features,
encompassing English language styles, conveyed sentiments, and lexical
diversity within spatial online social media review data. To this end, we
undertake a case study that scrutinizes reviews composed by two distinct and
demographically diverse groups. Our analysis entails the extraction and
examination of various statistical, grammatical, and sentimental features from
these two groups. Subsequently, we leverage these features with machine
learning (ML) classifiers to discern their potential in effectively
differentiating between the groups. Our investigation unveils substantial
disparities in certain linguistic attributes between the two groups. When
integrated into ML classifiers, these attributes exhibit a marked efficacy in
distinguishing the groups, yielding a macro F1 score of approximately 0.85.
Furthermore, we conduct a comparative evaluation of these linguistic features
with word n-gram-based lexical features in discerning demographically diverse
review data. As expected, the n-gram lexical features, coupled with fine-tuned
transformer-based models, show superior performance, attaining accuracies
surpassing 95\% and macro F1 scores exceeding 0.96. Our meticulous analysis and
comprehensive evaluations substantiate the efficacy of linguistic and
sentimental features in effectively discerning demographically diverse review
data. The findings of this study provide valuable guidelines for future
research endeavors concerning the analysis of demographic patterns in textual
content across various social media platforms.