Sentiment analysis Use Cases

Sentiment Analysis has a wide range of applications as:

Social Media

If for instance the comments on social media side as Instagram, over here all the reviews are analyzed and categorized as positive, negative, and neutral.

Nike Analyzing Instagram Sentiment for New Shoe Launch

Nike, a leading sportswear brand, launched a new line of running shoes with the goal of reaching a younger audience. To understand user perception and assess the campaign’s effectiveness, Nike analyzed the sentiment of comments on its Instagram posts related to the new shoes.

• Nike collected all comments from the past month on Instagram posts featuring the new shoes.
• A sentiment analysis tool was used to categorize each comment as positive, negative, or neutral.

The analysis revealed that 60% of comments were positive, 30% were neutral, and 10% were negative. Positive comments praised the shoes’ design, comfort, and performance. Negative comments expressed dissatisfaction with the price, fit, or availability.

The positive sentiment majority indicates that the campaign resonated well with the target audience. Nike can focus on amplifying positive aspects and addressing concerns raised in negative comments.

Customer Service

In the play store, all the comments in the form of 1 to 5 are done with the help of sentiment analysis approaches.

Play Store App Sentiment Analysis for Improved Customer Service

Duolingo, a popular language learning app, received a significant number of negative reviews on the Play Store citing app crashes and difficulty completing lessons. To understand the specific issues and improve customer service, Duolingo employed sentiment analysis on their Play Store reviews.

• Duolingo collected all app reviews on the Play Store over a specific time period.
• Each review’s rating (1-5 stars) and text content were analyzed.
• Sentiment analysis tools categorized the text content as positive, negative, or neutral.

The analysis revealed a correlation between lower star ratings and negative sentiment in the textual reviews. Common themes in negative reviews included app crashes, difficulty progressing through lessons, and lack of engaging content. Positive reviews praised the app’s effectiveness, user interface, and variety of languages offered.

By analyzing Play Store reviews’ sentiment, Duolingo identified and addressed customer concerns effectively. This resulted in a significant decrease in negative reviews and an increase in average star ratings. Additionally, Duolingo’s proactive approach to customer service improved brand image and user satisfaction.

Marketing Sector

In the marketing area where a particular product needs to be reviewed as good or bad.

Analyzing Consumer Sentiment for Product Review in the Marketing Sector

A company launching a new line of organic skincare products needed to gauge consumer opinion before a major marketing campaign. To understand the potential market and identify areas for improvement, they employed sentiment analysis on social media conversations and online reviews mentioning the products.

• The company collected social media posts and online reviews mentioning the new skincare line using relevant keywords and hashtags.
• Text analysis tools were used to clean and pre-process the data.
• Sentiment analysis algorithms categorized each text snippet as positive, negative, or neutral towards the product.

The analysis revealed an overall positive sentiment towards the product, with 70% of mentions being positive, 20% neutral, and 10% negative. Positive comments praised the product’s natural ingredients, effectiveness, and skin-friendly properties. Negative comments expressed dissatisfaction with the price, packaging, or fragrance.

The bar graph clearly shows the dominance of positive sentiment towards the new skincare line. This indicates a promising market reception and encourages further investment in marketing efforts.

Sentiment analysis is a popular task in natural language processing. The goal of sentiment analysis is to classify the text based on the mood or mentality expressed in the text, which can be positive negative, or neutral.