Social media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are 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 the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor 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.
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.
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