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What Does Sentiment Analysis of Twitter Data Reveal?

by | Dec 25, 2020

3:30 min read

Sentiment Analysis is key to practice social listening on social media platforms. Listening is the practice of actively receiving and interpreting messages in a conversation, verbal or written. In social media, it applies the same way. Brands can now actively listen and monitor how customers perceive a new product, services, or features development.

In machine learning, sentiment analysis is a natural language processing (NLP) technique that can measure polarity within a text in a range of positive, neutral, and negative. It can also provide a subjectivity score to evaluate personal opinions

Why use Twitter datasets for Sentiment Analysis?

In Twitter, large amounts of user-generated data are created and expressed via tweets. Twitter creates a space for people to communicate efficiently, quickly, and in a visible way. These tweets range from promoting events to discovering what is happening in the world. Tweets are a way to showcase brand voices while interacting with users more playfully and authentically in a brand’s specific field. 

It acts like a public forum allowing people to provide feedback, complaints, openness, and communication accountability. 

Companies using Twitter data for sentiment analysis can examine how their communication is perceived like new product features or services as negative, neutral, or positive. 

Performing sentiment analysis of Twitter data with text machine learning includes analyzing elements such as hashtags. Hashtags are a tool to rank up content relevancy in particular topics, organize tweets, and capture target audiences. This NLP technique analyzes tweets for creating and training sentiment classifiers.

Sentiment Analysis in Action

Sentiment analysis use cases range from the stock exchange, banking, and finance to retail and political campaigns. This makes PR, marketing, and customer relations easier and more assertive.

OnePlus famously used sentiment analysis to detect what their consumers discussed in their new smartphone OnePlus6 launch. Mask Group 11

According to Brandwatch, creating and analyzing sentiment analysis datasets helped OnePlus focus on what mattered to its consumers and how to meet their needs. In this case, it was a microprocessor chip, pricing, and design. 

Academic Papers have been developed around sentiment analysis of Twitter data, such as in the 2012 U.S. Presidential Election Cycle. 

One of the key findings from a study from the University of Southern California showed that tweet volumes increased amidst campaign events, compared to regular days. The group of students suggested that real-time sentiment analysis of Twitter data could provide an in-depth insight into how public sentiment evolves, changes, and deflects from contenders.  

In South Africa, Mr. Sello Ralethe, Risk Digitisation Specialist at Rand Merchant Bank, conducted an experimental Twitter sentiment analysis project on five of the biggest banks on Twitter: ABSA, Capitec Bank, First National Bank (FNB), Nedbank, and Standard Bank tweets covers the month of August 2019. 

Respectively the prominent perception of customers was the following:

1. ABSA – 65.8% negative😡 👎

2. Capitec – 50% positive😁👍

3. FNB – 48% positive 😁👍

4. Nedbank – 50.6% negative 😡 👎

5. Standard Bank – 62.2% negative 😡 👎

Although it is a short and experimental project, by analyzing Twitter datasets for sentiment analysis, banks and financial markets can measure the impact of environmental investment, return and risk, and even may be able to predict movements in the stock market.

Two clear benefits of using Twitter data for sentiment analysisMask Group 10

 1. Measuring logical and emotional responses. Sentiment analysis can provide an in-depth understanding at a large scale of tangible and intangible opinions and responses evaluating people’s states of mind.

2. Highlights what issues matter to the customer. By utilizing sentiment analysis in social media such a Twitter, brands can specifically address underlying sentiments from the customer perspective and figure out how to handle them, and improve decision-making processes.  

In conclusion, brands using sentiment analysis in platforms such as Twitter with machine learning can expand their understanding of how customers perceive their tone of voice. They can also optimize time by not needing to revise tweets manually one by one.

The abundance of data at a fast pace makes it challenging to monitor customer’s opinions. With sentiment analysis, brands can monitor opinions and practice social listening to handle bad reviews or complaints, leading to improved customer relations, brand reputation, and product features.

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