Sentiment analysis is a big branch within the field of linguistic communication. It gives us some pragmatic as well as some advanced implementation methods and techniques to unravel difficulties faced during artificially intelligent activities in the long run, and it has become a really interesting and important field of exploration. Any business is obliged to understand clients — their needs, their opinions, their satisfaction with the product. In case of large web-based companies we need to analyze hundreds of thousands or even millions of opinions to different products.Also taking opinions from every customer is very important, and for a country like india where there are so many languages and there are so many people who cant read or write but they are also customers to many products and their opinions are also valuable for the company, so we in this application perform sentiment analysis on both text and speech and also in the languages of Hindi and English. The user can type or speak into the application in either of the languages and the sentiment will be predicted. We are building this application in the python framework, flask. We feel that this application will be of great use for companies, as in today's fast paced competitive world, every company is becoming more and more customer-centric and every opinion has become very important and today that everyone have access to the internet, everyone's opinion is visible and open to the world and the company has to take care of every opinion in the market and every opinion matters.
So, basically my goal is that every opinion should matter, language should not be a barrier and everyone should have the freedom to express their opinion or sentiment.
What it does
In this application we perform sentiment analysis of reviews or statements. This application can analyze sentiment from both text and speech. We can use it for performing sentiment analysis of reviews in the languages hindi and english. We can just speak into the application and it will predict the sentiment.
How I built it
I built using the web development framework, flask. I have developed this application fully in python. I have used machine learning and artificial intelligence techniques for developing this application apart from the natural language toolkit provided in python. I also used google speech to text api to convert the speech into text. I have used OpenVINO for the deep learning parts.
Challenges I ran into
I did not know flask before, I had to learn flask and becuase of which I ran into a few difficulties.
Accomplishments that I'm proud of
Built a highly accurate sentiment analysis model that can predict the sentiment in 2 languages.
What I learned
I in general code in django, but I wanted to learn flask as well, so for that purpose I developed this application using the flask framework and I have successfuly built the application
What's next for Sentimentation
We can increase the accuracy of our application though it is mostly correct only. We currently are performing sentiment analysis for speech and text in two languages English and Hindi, we can expand it further so that sentiment in more languages can be predicted using our application.
In our project we have used machine learning and artificial intelligence approaches for perforiming the sentiment analysis. We have also tried using the OpenVINO toolkit for doing the same using Deep learning techniques.
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