List of Contibutors: Affan Mohammed N Marikar, Akash S, Aldas T Francis, Athul Raju, Rehana K C
A Machine learning model for sentiment recognition from speech using SVM for the purpose of speech recognition. In this model we will be accepting audio message from the user and it will be converted to text using python libraries such as speech recognition. This text will be passed for sentimental analysis and proper output will be generated. Multi-class sentiment analysis problem to classify texts into five emotion categories: joy, sadness, anger, fear, neutral.
The goal is to build a model based on Speaker’s input and thereby finding the emotional state. Identifying mental-wellbeing of humans. Identifying key emotional triggers . To study human behavior in which we extract user opinion and emotion from plain text.
Audio message is converted into text message using python libraries. With the help of NLP, emotion recognition is done. Web page is developed using flask.
E-Commerce Industry: Analyzing the customer review as textual data from chat-bots,logs from contact centers,emails.
Social Media Monitoring: Brand monitoring based on the details given by consumer.
Live insights: Customer mood can change at any point during a customer service interaction,user can see the mood of each customer in a session
Market and competitor research: Find out who’s trending among your competitors and how your marketing efforts compare.
Ads Placement: Place an ad when one praises or criticizes a product.
