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Sentiment Analysis on Social Media using Machine Learning Approach

Social media has been embraced by different people as a convenient and
official medium of communication. People write messages and attach
images and videos on Twitter, Facebook and other social media which they
share. Social media therefore generates a lot of data that is rich in
sentiments from these updates. Sentiment analysis has been used to
determine opinions of clients, for instance, relating to a particular
product or company. Knowledge based approach and Machine learning
approach are among the strategies that have been used to analyze these
sentiments. The performance of sentiment analysis is however distorted
by noise, the curse of dimensionality, the data domains and size of data
used for training and testing. This research aims at developing a model
for sentiment analysis in which dimensionality reduction and the use of
different parts of speech improves sentiment analysis performance. It
uses natural language processing for filtering, storing and performing
sentiment analysis on the data from social media. The model is tested
using Naïve Bayes, Support Vector Machines and K-Nearest neighbor
machine learning algorithms and its performance compared with that of
two other Sentiment Analysis models. Experimental results show that the
model improves sentiment analysis performance using machine learning
techniques.