How to build a Twitter Sentiment Analysis?

The basis of a Twitter Sentiment Analysis would be Natural Language Processing.

In this case, we would build a machine-learning model that would classify the different types of tweets (for example, happy vs sad)

Natural Language Processing (NLP) is the interdisciplinary field of computer science and linguistics. NLP gives computers the ability to interpret, manipulate, and comprehend human language. It is used in opinion polls, for creating entire marketing strategies, and more. It has reshaped how businesses work and understand their customer.

Due to this, many texts can be processed for features such as names, topics, themes, etc in seconds. Before it would take hours and a team of people to manually complete the same task.

Steps

1. Gather data

It’s important that your Twitter data is representative of what you’re trying to find out because you’ll use it to train your sentiment analysis model and test how your model performs on Twitter data. You should also consider the type of tweets you want to analyze.

Places to gather data from Kaggle, past research papers, etc. You could also use the Twitter API to get access to and interact with public Twitter data.

Try to gather labeled data as it will be easier for classification purposes.

2. Prepare Your Data
Once you’ve gathered the tweets you need for your sentiment analysis, you’ll need to prepare your data. Social media data is unstructured and needs to be cleaned before using it to train a sentiment analysis model. A good quality data will lead to more accurate results.

Preprocessing a Twitter dataset involves a series of tasks like removing all types of irrelevant information like emojis, special characters, and extra blank spaces. It can also involve making format improvements, deleting duplicate tweets, or tweets that are shorter than three characters.

3. Create a Twitter Sentiment Analysis Model
Build a machine-learning model that you can use.

4. Train your machine learning model

5. Test your classifier

Once you have trained your model with a few examples, you can paste your own texts to see how the sentiment analysis model classifies it.

6. Increase accuracy

After building the machine learning model, you can go back and make changes to increase the accuracy. Some suggestions include hyperparameter tuning, early stopping, etc.

7. Display or Visualize Your Results
Data visualization tools help explain sentiment analysis results simply and effectively such as graphs.

Future Use:

Social Media Monitoring
1. What do customers love about your brand?

2. What aspects get the most negative mentions?

Customer Service
1. Detect customer queries fast

2. Customer service can easily engage with customers and respond quickly to customer queries

Market Research
1. Twitter is used for consumer insight as people use the app to express their feelings, observations, beliefs, and opinions about a variety of topics.

Brand Monitoring
1. Track customer reactions to new features on Twitter.

Political Campaigns
1. How people feel about a specific candidate.