Exercise: Sentiment Analysis of Tweets

Task

Sentiment analysis is an NLP task to identify the tone and sentiment behind the given input. The possible output classes of sentiment analysis are positive or negative. The positive analysis results from texts that show favorable attitudes, and the negative sentiment from unfavorable ones. Usually, tweets are used as input to the sentiment analysis task to identify the polarity of tweets.

Output classes

The possible output classes for sentiment analysis in this case are:

Positive: The positive output class shows an optimistic response and favorable attitude toward a given input statement.

Neutral: The neutral input class shows a lack of emotional polarity. It conveys an indifferent tone and does not express a clear emotional stance.

Negative: The negative output class shows a pessimistic response and an unfavorable attitude toward a given input statement.

Application of sentiment analysis

Sentiment analysis is a mode companies use to analyze feedback from their customers regarding products and services. The feedback is usually gained through channels like open forums, social media platforms, and customer service portals. A few real-world use cases of sentiment analysis are given below.

Market analysis: Sentiment analysis helps companies analyze trends and competitors. After analyzing competitor reviews, they identify the possible shortcomings of the competitor’s product and come up with a better and updated version.

Social media analysis: User comments and posts on social media are a powerful way to reach out to new customers. Understanding the sentiment of users on products helps polish the products. The updated products can be more attractive to the target audience.

Customer service analysis: The customer service experience can help a company excel; hence, it is better for customer service to respond efficiently and effectively to user queries. A sentiment analysis algorithm can help analyze the experience and respond based on the urgency of the query.

Examples

For each output class, the OpenAI looks at the sequence of words, checks how they relate to the discussed subject, and passes an output as a response. The example for each output class is given below.

Positive: The OpenAI prompt analyzes the tweet, and since the word “best” is used to describe the movie, it shows a favorable and optimistic approach. Hence, the response is positive.