How to perform Twitter sentiment analysis?

What is Twitter sentiment analysis?

Twitter sentiment analysis is a real-time automated machine learning technique that identifies and classifies the subjective context in tweets.

Sentiment analysis of Twitter data involves opinion mining to analyze positive, negative or neutral psychological intent in tweets. Subsequently, subsequent textual cues are predicted based on the patterns identified during text mining.

With an average of approximately 10,033 tweets per second, as reported in May 2022, the growing number of tweets per day is generating a lot of data.

Twitter sentiment analysis machine learning uses natural language processing (NLP) classification algorithms to meet the challenge. Among them are Logistic Regression, Naive Bayes (NB) and Support Vector Machines (SVM) are well known classifiers.

How to perform Twitter sentiment analysis?

Common steps to perform a Twitter sentiment analysis include:

- Sorting Twitter data

- Cleaning Twitter data

- Developing Twitter sentiment analysis model

- Analyzing Twitter data for positive/negative sentiment

- Visualizing insights

1. Sorting Twitter data

The first step in sentiment analysis is to collect and sort the data. There is a huge amount of data on Twitter and it is important to choose the data that is most relevant to the problem you are trying to solve or the thing you are hoping to find. Only relevant data can be used to train a sentiment analysis model and test whether the model performs satisfactorily on the Twitter data. Another important aspect to cover is the type of tweets you want to analyze - historical or current. To sort this data, you first need to extract it from Twitter. To do this, you can use some of the following platforms:

- Zapier, for example, creates an automated workflow between Twitter and Google Forms.

- IFTTT collects Twitter data without any code.

- Export tweets to track hashtags, keywords, etc. in real time, or find historical tweets and mentions.

- Tweets download to collect tweets from your own account, including mentions and replies.

- Twitter API for accessing and analyzing public tweets about keywords, brand mentions, subject tags, or tweets from specific people. - Tweepy, a python library for accessing the Twitter API and collecting data from there.

2. Cleaning Twitter data

After collecting and sorting the data, it needs to be cleaned before it can be used to train a Twitter sentiment analysis model. Twitter data is mostly unstructured, so the cleaning process involves removing emoticons, special characters, and unnecessary spaces. The process also involves removing duplicate tweets, formatting, and removing very short tweets - tweets of less than three characters. Cleaner data provides more accurate results.

3. Developing Twitter sentiment analysis model

There are different machine learning platforms available to help people build and implement Twitter sentiment analysis models. These platforms can provide access to pre-trained or ready-to-train models. You can use your Twitter data to train these models. To develop a model, you need to perform the following steps:

- Select the type of model you want to build. For example, a classifier model that helps classify text into predefined labels.

- Determine the classification type. In this case, it will be sentiment analysis.

- Import relevant Twitter data to train the model.

- Label the data as positive, negative or neutral. For example, to train your model.

- Test your model.

4. Analyzing Twitter data for positive/negative sentiment

Once your model has been trained and gives satisfactory test results, it is ready for deployment. Now, you just need to connect your Twitter data to your sentiment analysis model. There are several ways to do it. One way is to analyze specific files of new or invisible tweets and classify them. Another way is to integrate Twitter data with Zapier and Google Tables and use your model to analyze this data.

5. Visualizing insights

There are tools that can help visualize your data results and make them easy to interpret and digest. These attractive visualization tools, such as Google Data Studio, Looker, Tableau, etc., create visual reports, including charts, graphs, and data tables, that are easily understood by a wider audience.