As the eCommerce business proliferates, the binge buying syndrome has also increased. However, consumers who are always on the lookout of value for money prefer to do an online analysis of products and gauge the sentiment of other buyers before taking the leap. Similarly, the product companies also keep their ears to the ground and analyze their brand resonance in the market through different techniques. However, to make sense of the data deluge is a task akin to finding a needle in a haystack. Here technology, more specifically Artificial Intelligence (AI)/Machine Learning (ML) and Data Science disciplines, enables you to conduct sentiment analysis at scale with very high accuracy levels. It helps you overcome the limitations of traditional models, which cannot recognize sarcasm.
What is Sentiment Analysis?
According to Wikipedia Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In simpler words, it’s an analysis and classification of a text into positive and negative, neutral.
Sentiment analysis, a valuable technique has assisted businesses to get better insights into data, understand their brand, product or service, identify new opportunities, understand Voice of Customer (VoC), and manage reputation.
It is very essential for businesses to know what are the feedbacks, reviews, thoughts, opinions of their customers. Example – Sentiment analysis can analyze huge amounts of reviews, about your product or services, which could help you discover if your customers are happy or not.
A huge amount of data is created in the form of chats, feedback, surveys, articles, online opinions, ratings, recommendations, documents, emails, social media, comments, etc. This data is often unstructured and difficult to understand, time-consuming, and expensive.
How does Technology assist Sentiment Analysis?
It is humanly next to impossible to read all product reviews and decide on aspects related to a product purchase or even a product upgrade. The systematic use of AI/ML branches such as Deep Learning and Natural Language Processing (NLP) helps you to automate and simplify the task. This technology mashup undertakes a modular approach:
- Review classification
- Feature capture
- Topic capture
- Sentiment analysis
A. Review classification module uses AI algorithms and tags the online review into pre-defined categories such as positive, neutral, and negative. It also tags it as a query, suggestion, or complaint.
B. Feature capture module analyzes the review content and extracts the key feature it is talking about using NLP and Topic Modelling.
C. The topic capture module uses LDA or Latent Dirichlet Allocation and extracts the topic that the review is talking about.
D. Sentiment Analysis module uses RNN or Recurrent Neural Networks and LSTM or Long Short Term Memory to extract sentiment including the sarcasm aspects.
In summary:
Global companies such as Apple and Microsoft use such AI models to extract and decipher unstructured data by using unsupervised learning to find relevant information. The sentiment analysis uses Deep Learning and NLP to go beyond the limitations posed by conventional methods.
Image Credit – Freepik.com
