Getting Started with Sentiment Analysis

INTRODUCTION

Machine learning is a powerful technology that simplifies data-related systems. This technology is not just limited to numeric data, it can be employed in text-based datasets that may even be subjective. Natural Language Processing (NLP) is a branch of artificial intelligence that involves the development of algorithms and techniques that enables computers to understand, interpret and generate human language. Sentiment analysis is one of the applications of this branch.

WHAT IS SENTIMENT ANALYSIS

Sentiment according to Oxford learners dictionaries is a feeling or an opinion, especially one based on emotions. Likewise, sentimental analysis deals with analyzing an opinion or view that is expressed by someone. Formally, sentimental analysis is a technique that is used to analyze text data, to determine the overall sentiment expressed in it.

The goal of the sentimental analysis is to distinguish whether the feeling or opinion expressed in the text is positive, negative or neutral. Trivial as it may sound, this analysis is important for several reasons including:

• Marketing, where companies use it to identify topics and language that resonate with their prospective customers thus informing their text/print marketing campaigns.
• Reputation management: companies leverage sentiment analysis to monitor their online reputation and address negative sentiments promptly.
• Rating customer experience: by analyzing customer feedback and sentiment, organizations get to understand how clients perceive their products, services or brand. They can then identify ways to provide better customer experiences.
• Product development: as a developer, you would want to know how people perceive your product. You can use sentiment analysis to get insights into customer preferences and pain points, which you in turn use to inform your product development and innovation.
• Competitive analysis: you can use sentiment analysis to monitor your competitors by getting how customers perceive them. You can then identify opportunities for differentiation and competitive advantages.

Enough of what and why sentiment analysis, we’ll now answer the how. The next part outlines simple and basic steps to carry out sentiment analysis. Sentiment analysis algorithms typically use machine learning and statistical techniques to automatically categorize people’s views as positive, negative or neutral based on the choice of words they use, context and other factors. We will use a sample dataset containing tweets and classify them as either positive or negative.

1. Define the problem
2. Collect and preprocess
3. Label data
4. Choose a model
5. Train and evaluate your model
6. Deploy your model

Define the problem

{

STEPS TO DO SENTIMENT ANALYSIS
TOOLS NEEDED
PROCEDURE
SUMMARY/CONCLUSION
}