Twitter Sentiment Analysis: How To Do It? The-Step [No-Code] Process

Want to know how people on Twitter feel about your company? Tweets may be monitored in real time for sentiment analysis, revealing users’ true opinions about your company, goods, and services. And you can do it even if you don’t know how to code!

Sentiment analysis on Twitter: what is it?

A tweet’s positive, negative, or neutral tone can be identified with the use of sentiment analysis.

The manual method involves reading over each tweet and deciding whether it is favourable or bad. Nonetheless, it is a lengthy procedure.

A few helpful instruments can perform the work for you. By examining the tweet’s wording and context, such software may automatically discern the emotion.

And why is this so crucial, anyway?

You may learn a lot about how people feel about a certain issue by analysing tweets about it.

Twitter sentiment analysis may help you understand the intended audience feels by counting how many good, negative, and neutral tweets there are about a topic.

Twitter’s sentiment analysis technologies’ aims

Tools that analyse the sentiments expressed on Twitter are invaluable for gauging public opinion on any given issue, news story, or product.

To hear what people are saying about your product or service, you can’t do better than Twitter, one of the most widely used social networking sites.

Those who utilise Twitter are more likely to be honest and forthright in their evaluations than those who use other review platforms. Because people using Twitter may hide their identities if they so want.

The ability of sentiment analysis algorithms to ascertain the writer’s intent in a piece of information is always improving. This explains why interest in sentiment analysis is growing

Opinion mining is used in the business world to learn how customers feel about a given product, service, brand, marketing effort, or even a rival.

If you want to know what your consumers appreciate (or don’t like) about your business, sentiment analysis can help you out.

Speaking about commerce, this type of study may really be utilised to foresee movements in the stock market!

Sentiment analysis is used in politics to monitor public opinion on the government, politicians, remarks, and policy changes, and it may even be used to foretell the outcomes of elections.

Studies showing the widespread usage of Twitter data for sentiment analysis to forecast election outcomes throughout the globe can be found online. One such study predicts the Brexit vote by monitoring and categorising popular sentiment using Twitter data.

Activities in the public eye: Opinion analysis is used to examine how people felt about the latest Pokemon Go, the first episode of Game of Thrones, or the Academy Awards.

How to analyse the mood on Twitter.

One of two primary approaches:

By going through tweets and labelling them as favourable, bad, or neutral, you may give it a try doing it by hand. But, it takes a long time and isn’t always reliable. With respect to social media, Adidas, for instance, has amassed over 205k new mentions on Twitter in the past 30 days. Is it realistic to think that we can examine all of them to identify which are beneficial and which are negative? It doesn’t seem likely to me.
The emotional tone of tweets may be automatically analysed with the use of monitoring systems that use algorithms. They compile data from Twitter and do sentiment analysis on it. The programme has the potential to outperform human labour in terms of precision and timeliness.

To lessen the time spent on manual sentiment analysis, you might think about adopting a social media monitoring service.

Several social media monitoring solutions now include include Facebook and Instagram sentiment analysis in addition to Twitter.

Explain the process of Twitter sentiment analysis.

The algorithms used for sentiment analysis differ amongst applications. From a purely technical standpoint, here’s how Brand24 operates:

As soon as a project is created in our system, it begins to gather any references containing the project’s keyword. When a mention is discovered, it is run through our sentiment analysis algorithm and the results are shown on a dashboard. Internet monitoring and sentiment analysis are performed in real-time so that you may see actionable insights as soon as they become available.

Our sentiment analysis algorithm takes use of state-of-the-art advances in Machine Learning and AI more generally, including Deep Learning and Pretrained Linguistic Models (PLM), which are also employed by industry heavyweights like Google, Microsoft, Facebook, and Baidu. As a consequence, the AI capabilities achieve the closest approximation to the true comprehension of mentions as is now achievable given the level of scientific knowledge.


Using a Twitter monitoring tool makes it simple to examine the microblogging service’s output. In fact, this type of software can automatically distinguish between positive and negative tweets. In addition, the findings of the sentiment analysis are presented in an accessible format.