Sentiment Analysis on Social Media Content

TL;DR

This paper presents a hybrid machine learning model for sentiment analysis of Twitter data, effectively classifying tweets as positive, negative, or neutral, and comparing popularity of McDonald's and KFC.

Contribution

The proposed model uniquely combines supervised and unsupervised learning techniques for improved sentiment analysis on unstructured Twitter data.

Findings

Model achieves strong performance on Twitter sentiment classification

Effective differentiation of restaurant popularity based on sentiment data

Utilizes multiple machine learning algorithms with validation metrics

Abstract

Nowadays, people from all around the world use social media sites to share information. Twitter for example is a platform in which users send, read posts known as tweets and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Companies can benefit from this massive platform by collecting data related to opinions on them. The aim of this paper is to present a model that can perform sentiment analysis of real data collected from Twitter. Data in Twitter is highly unstructured which makes it difficult to analyze. However, our proposed model is different from prior work in this field because it combined the use of supervised and unsupervised machine learning algorithms. The process of performing sentiment analysis as follows: Tweet extracted directly from Twitter API, then cleaning and discovery of data performed. After that the data were fed into several models for the purpose of training. Each tweet extracted classified based on its sentiment whether it is a positive, negative or neutral. Data were collected on two subjects McDonalds and KFC to show which restaurant has more popularity. Different machine learning algorithms were used. The result from these models were tested using various testing metrics like cross validation and f-score. Moreover, our model demonstrates strong performance on mining texts extracted directly from Twitter.