Objective: Leveraging Python for online review analysis with NLP.
Research question: Can Natural Language Processing (NLP) be used to identify review sentiment and customer satisfaction trends?
Abstract: This project provides an overview of how businesses can utilise Natural Language Processing (NLP) to analyse online reviews and gain valuable insights into customer experiences. It highlights the time-consuming nature of reviewing large amounts of customer feedback and emphasises how NLP can help businesses to analyse data faster and more accurately. In addition, it provides real-world examples of how NLP can be used for analysing online reviews, including sentiment analysis, topic modelling, and entity recognition. It also emphasises the importance of selecting the appropriate NLP tools and techniques that best suit a company’s specific needs. Highlighted are the benefits of using NLP for online review analysis, such as identifying emerging trends in real-time, reducing costs, and improving decision-making.
Further Info
Sentiment analysis is a technique used to automatically identify and extract subjective information from text, such as opinions, emotions, and attitudes. It has become increasingly popular in recent years as more and more businesses recognise the importance of understanding customer sentiment. There are several approaches to sentiment analysis, including rule-based, machine learning-based, and hybrid methods. Rule-based methods use handcrafted rules to identify sentiment, while machine learning-based methods use algorithms to learn from data. Hybrid methods combine both approaches. Sentiment analysis can be applied to a wide range of data sources, including social media, customer reviews, and news articles and can determine whether the sentiment expressed is positive, negative, or neutral, which can provide valuable insights into customer satisfaction, brand reputation, and market trends. However, it is essential to be aware of the limitations of sentiment analysis, such as the difficulty of detecting sarcasm and irony.