TY  - JOUR
T1  - Sentiment Analysis on Nigerian Tweet Using Data Mining Techniques
AU - Chinedum, Amaechi AU - Ogochukwu, Okeke 
JO  - International Journal of Soft Computing
VL  - 16
IS  - 2
SP  - 25
EP  - 28
PY  - 2021
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2021.25.28
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2021.25.28
KW  - Sentiment analysis
KW  -pre-processing
KW  -opinion mining
KW  -Nigerians Tweets
KW  -Twitte
AB  - Probing sentiments in social media poses a task
to natural language processing because of the complexity
and variability in the different dialect expression, noisy
terms in form of local slang, abbreviation, acronym,
emoticon and spelling error coupled with the availability
of real-time content. Most of the knowledge based
approaches for resolving local Nigerian slangs,
abbreviation and acronym do not consider the issue of
ambiguity that evolves in the usage of these noisy terms.
This research implements an improved framework for
social media feed pre-processing that leverages on the
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-Igbo
tweets with an accuracy of 90%.
ER  -