Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

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 Naive 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.

Automated summary

This article discusses the importance of sentiment analysis in social media and the generation of sentiment-rich data. The author presents a new model that incorporates dimensionality reduction and natural language processing with part of speech tagging to improve sentiment analysis performance. Experimental results demonstrate the effectiveness of the model in utilizing machine learning techniques for sentiment analysis.