Twitter Sentiment Analysis With R
About Client
Based in the USA, the client is a leading Fortune 500 manufacturing company, with net revenue of more than $60 billion and a plethora of around 23 manufactured products that create more than $1 billion each in yearly retail sales. It has multiple retail stores across the country.
Client's Business Goals
To serve customers in a better way, the company wanted to understand customer feedback on newly launched products from social media platforms like Twitter. They are looking for in-depth analysis like
- Whether customers really liked the product
- Whether the customers preferred the company’s product over other products
- Whether the preferences changed over time
Basically, the company wanted to know how happy their customers are and what they can do to perk up their issues.
Challenges
The challenges they encountered while collecting customer feedback are as below
The company was not able to find
- Whether the text is about the company’s product or a competitor’s product
- Is the post about the consumer’s expectation or is the review about a real experience from the product?
The information was in the form of thousands of individual texts and posts per year on Twitter Thus, digging out refined data was difficult.
The company focused on surveys which were collected periodically from the Sales Executive and Marketing campaign on Twitter
Our Solution
After understanding the company’s problem, SPEC INDIA came up with the solution of Sentiment Analysis in R.
SPEC worked along with the customer to analyze the marketing campaign, feedback data and the tweets related to the products along with competitors’ brands.
Once the appropriate text was identified, sentimental analysis and visualization were performed to determine whether the text was positive or negative.
To come up with the best result, we done the following
Converted tweet to a text format
Combined the text for different brands with the same category of products
Calculate the sentimental scores for the tweets
Identify positive, negative, and neutral sentimental
Developed visualization – Tag Cloud, Comparative Analysis
Data factors considered – Landing Page, Converted, Group and Time Stamp
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Business Benefits
Monitors the product and brand performance
Optimize, evaluate the efficiency and measure the ROI of the marketing campaign
Help the company to understand the customer’s need
Enable to develop more insightful, data-based marketing strategy
Reduce customer churn
Track the overall customer satisfaction
Boost sales revenue
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