Sentiment analysis for product rating
The IT world never stopped spoiling users by working hard on providing them the smoothest user experience from giving them the freedom of feedback and rating, to actually paying attention to their personal feelings!
One of the most effective ways to elevate user experience in e-commerce is sentimental analysis for product rating.
What is sentimental analysis?
Sentiment Analysis is a data management process that analyzes and filters the available sentiment-based data coming from user reaction and feedback toward a particular product or service in order to generate useful insights, rank products according to general preferences.
It has the ability to catch emotions, attitudes, opinions and thoughts from feedback and comments through Natural Language Processing (NLP) technologies.
If you don’t know what NLP stands for Read more about its applications.
How sentiment analysis really works?
Most e-commerce web/ apps feature feedback options where the user can write down his opinion and rate a certain product as well view the ratings attributed by other users. Sentiment Analysis for Product Rating operates as a system that reads between the lines of comments in order to catch sentimental hints and score them as positive, negative, or neutral by recognizing necessary keywords.
The sentiment based keywords in comments such as: “sad”, “happy”, “disappoint”, “great”, “satisfied” etc get compared to the stored database of the system and then eventually help the algorithm to classify the general emotional state of the user in order to conclude if the product is liked or disliked.
The system stores the results in a way that is useful for clients to use and understand. and so any User can easily find out the correct product for his usage according to the specific rank.
Sentiment analysis comes in help to the user and organization in many ways:
- Find suitable products according to other users rating
- Improve business services
- Categorize users per type and identify the most profitable range of products you have
- Detect and track the user sentiments overtime
- Determine which user segment appreciates your brand and which don’t.
- Track change in user behavior for business reports
- Allows to Find the detractor elements to eliminate them and amplify the promotive ones
- measure the ROI of your marketing campaigns
- Predict crises
What are the technologies behind it?
ML-based sentiment analysis
Machine Learning algorithms help the system compare keywords according to previously stored data and react similarly. It requires the admin to train the classifier on different case scenarios and gather a dataset with relevant examples for positive, neutral, and negative classes.
Lexicon-based sentiment analysis
As the name suggests, these techniques use dictionaries of words. Each word is annotated with its emotional polarity and sentiment strength. This dictionary is then matched with the analyzed text to calculate its overall polarity score.
What are you waiting for to elevate your e-commerce business and satisfy your users? Contact us!