Data-Driven Decision Making with Sentiment Analysis in R
Should Businesses Really Hear Their Customers' Voices?
In a rapidly evolving world that is getting more and more AI-driven every instant, businesses now need to constantly seek a competitive edge to remain sustainable. Companies may do this by regularly observing and analyzing customer opinions regarding their products and services. They achieve this by assessing comments from many sources, both online and offline. Identifying positive and negative trends in customer feedback allows them to fine-tune product features and design marketing strategies that meet the needs of customers.
Thus, customer opinions need to be discerned appropriately to find valuable insights that can help make informed business decisions.
Familiarizing Yourself with Sentiment Analysis
Sentiment analysis, a part of natural language processing (NLP), is a popular technique today because it studies people's opinions, sentiments, and emotions in any given text. Businesses can understand public opinion, monitor brand reputation, and improve customer experiences by applying sentiment analysis to their collected feedback, which contains valuable information, but its unstructured nature can make it difficult to analyze. By regularly analyzing customer sentiments, companies can identify their strengths and weaknesses, decide on how to boost product development, and build better marketing strategies.
Powerful packages for sentiment analysis in both Python and R enable businesses to uncover valuable patterns, track sentiment trends, and make data-driven decisions. In this article, we will explore how to use different packages (Quanteda, Sentimentr and Textstem) to perform sentiment analysis on customer feedback by processing, analyzing, and visualizing textual data.
Adding a Real-world Context
For this tutorial, let us consider a fictional tech company, PhoneTech, that has recently launched a new smartphone in the budget segment for its young audience. Now, they want to know the public perception of their newly launched product and, hence, want to analyze the customer feedback from social media, online reviews, and customer surveys.
To achieve this, PhoneTech needs to use Sentiment Analysis to find product strengths and weaknesses, guide product development, and adjust marketing strategies. For example, PhoneTech has collected feedback from various platforms like social media (e.g., informal comments like "The camera is
