Sentiment Analysis on E-commerce Products Review System
Sentimental Analysis Roadmap

Sentiment Analysis on E-commerce Products Review System

Sentimental analysis is also called opinion mining. It is rightly said that business grows by understanding and fulfilling needs and requirement of customers. It is a domain of analyzing text and determining emotional tone behind it, therefore getting better view of customer feelings, attitude and opinion about a particular subject. In other words, we can coin it as customer sentimental analysis. One must keep in mind that the best business understand the sentimental of their customers using sentimental analysis. Sentimental tools are used to identify writers attitude whether positive, negative or neutral.

Text Analysis
Text Analysis

Why Sentimental Analysis is required:

 Business will grow if company understand market. Market is reflection of customer requirement and needs. It is also the output of how a person feels about a particular product. To enhance growth and better results, sentimental analysis is necessary.

Relation of NLP and Sentimental Analysis:

NLP (Natural Language Processing) is a domain of artificial intelligence which is used for extracting features from text which can be further feed to a machine learning model for classification (positive, negative or neutral). Any Supervised classification model can perform this task. So it can be concluded that combination of NLP and Machine learning helps in performing sentimental analysis. Please look at the below image to understand my point of view.

Relationship Between NLP, ML and Sentimental Analysis
Relationship Between NLP, ML and Sentimental Analysis

  How Sentimental Analysis is achieved using NLP:

  • NLP Tokenization helps in converting text into words.
  • Stop Words Processing helps in removing unnecessary words.
  • Stemming and Lemmatization helps in converting different forms of a word (1st form, 2nd form, Singular form, Plural form). To explain this step, please consider this example says, saying and said all refers to same base word say.
  • Token Processing identifies negative context.
  • Forms feature set using above steps.
  • Feature set is feeded to any supervised classification model where target can be either negative, positive or neutral.

Use Case

  • Obama administration used this in 2012 before presidential election to know public opinion and view point.
  • Widely used in customer service department where huge mails are received at a time. Sentimental analysis tools categorize them into more urgent and less urgent. Problems are solved priority wise.
  • Widely used by E-commerce business companies where public opinion about a certain product is known by sentimental analysis tools. As a results better business can be achieved and caveats about what is going wrong is indicated.
  • Used by oil industries companies to know customers feelings and emotions about price fluctuations.
  • Used by Stocks Prediction companies to Gauge Reaction of public about stocks at different time since stock prediction is based on time series data.

Challenges in Sentimental Analysis

Since every coin has two sides so there may be some areas which needs to be improved where sentimental analysis fails  such as Name Entity Recognition, Anaphora Resolution, Parsing and Sarcasm. Think about those text which are misspelled or written in short letters or abbreviations and are grammatically incorrect. I personally find such in social media sites (Facebook or twitter). So such points needs to be considered and sentimental analysis field has to overcome such areas.

Overcoming Sentimental analysis - Team work
Overcoming Sentimental analysis – Team work

AI Sangam E-Commerce Analyze demo:

AI Sangam is targeting various trending fields which comes under AI and has made a demo on sentimental analysis whose video you can find by clicking on this link Sentiment Analysis on E-commerce Products Review System. Please spare some time to look at this video and see how the system takes customers review on Flipkart as input and produces response (positive or negative accordingly). For better visualization results comes in form of pie chart. I hope you will watch the video till the end and will ask some question which will rise in your mind.

Please spare some time to click on this button and fill AI Sangam Survey. It will help you for better coding vision and better employment. It will hardly take 2 minutes to fill only 5 questions. Thanks a lot.

Conclusion:

In a nutshell and precisely I could say that by overcoming the challenge, this domain would boost the business to next level and both the customers and companies are going to be benefit from such. Customer will get the right product and what they demand whereas companies will become successful in fulfilling the demands of customers in the right direction. Moreover companies will come to know how people think about the changes they are making. This could be related to government new policies or rise or decline of oil prices or anything else. People emotions and knowing helps these in taking the right action.

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