Sentiment Analysis
Use AI natural language processing to quantify the emotion and opinion expressed in text.
Overview
The Sentiment analysis module is an opinion mining system based on natural language processing and machine learning that aims at quantifying the sentiment and emotions wrapped in a piece of text. This system takes a batch of sentences, processes it and assigns a sentiment score to each sentence, ranging from -1 (very negative) to +1 (very positive).
Features
- State-of-the-art regression model uses the latest advances in natural language processing and deep learning to analyze the sentiment behind text data.
- Batch processing and parallelization allows you to parse hundreds of sentences in a matter of seconds.
- Generate API to connect social Networks.
Benefit
- Applicable tp any piece of text, tweet, document, magazine…
- Fast analyze of e-reputation, event sentiment…
- Allows to identify quickly problems and avoid controversy.
- Ease of use, our app can be used by anyone without the need of technical knowledge regarding deep learning.
How it Works
First of all, we need to split the input sentence into tokens. Then, we must transform each sentence into a language understandable by the machine. After that, we use a deep neural network architecture, the bidirectional GRU with Attention, to represent the sentence with a single vector that carries the information necessary to run the regression and assign a sentiment score.
Finally, the last layer of the model performs the regression task, by computing the sentiment score from the previously built sentence vector.