@inproceedings{kolajo-etal-2019-sentiment,
title = "Sentiment Analysis on {N}aija-Tweets",
author = "Kolajo, Taiwo and
Daramola, Olawande and
Adebiyi, Ayodele",
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2047",
doi = "10.18653/v1/P19-2047",
pages = "338--343",
abstract = "Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledge-based approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17{\%}.",
}
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%0 Conference Proceedings
%T Sentiment Analysis on Naija-Tweets
%A Kolajo, Taiwo
%A Daramola, Olawande
%A Adebiyi, Ayodele
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kolajo-etal-2019-sentiment
%X Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledge-based approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17%.
%R 10.18653/v1/P19-2047
%U https://aclanthology.org/P19-2047
%U https://doi.org/10.18653/v1/P19-2047
%P 338-343
Markdown (Informal)
[Sentiment Analysis on Naija-Tweets](https://aclanthology.org/P19-2047) (Kolajo et al., ACL 2019)
ACL
- Taiwo Kolajo, Olawande Daramola, and Ayodele Adebiyi. 2019. Sentiment Analysis on Naija-Tweets. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 338–343, Florence, Italy. Association for Computational Linguistics.