- Published on August 10, 2020
- In Deep Tech
How To Implement LSTM RNN Network For Sentiment Analysis
Through this article, we will build a deep learning model using the LSTM Recurrent Neural Network that would be able to classify sentiments of the tweets.
- By Rohit Dwivedi
Sentiment Analysis is a predictive modelling task where the model is trained to predict the polarity of textual data or sentiments like Positive, Neural, and negative. Sentimental Analysis is performed by various businesses to understand their customer behaviour towards the products well. It gives them automatic feedback of the customer that helps them to take actions accordingly.
Since we are already overloaded with lots of unstructured data it becomes very tough to analyze the large volume of textual data. But sentiment analysis can be very useful for businesses to label these texts. Sentimental Analysis can be done to compute feedback, reviews of the movies, etc. Even Emotion detection is like part of sentiment analysis where we can analyze the emotion of a person being happy, angry
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Rohit Dwivedi
I am currently enrolled in a Post Graduate Program In Artificial Intelligence and Machine learning. Data Science Enthusiast who likes to draw insights from the data. Always amazed with the intelligence of AI. It's really fascinating teaching a machine to see and understand images. Also, the interest gets doubled when the machine can tell you what it just saw. This is where I say I am highly interested in Computer Vision and Natural Language Processing. I love exploring different use cases that can be build with the power of AI. I am the person who first develops something and then explains it to the whole community with my writings.
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