UNDERSTANDING SENTIMENT ANALYSIS API

Sentiment Analysis, also known as opinion mining, is the process of determining the sentimental tone behind a text. It is used to gain an understanding of the sentences in the input as positive, neutral or negative emotion.

Concept of Sentiment Analysis

Sentiment relates to feelings, attitudes, emotions, and, opinions. Sentiment Analysis applies Natural Language Processing (NLP) techniques to identify the Sentiment behind a text. Analyzing an individual’s opinion from a text can be very difficult but, with sentiment analysis, we can understand the opinion or mood of the person who is sending the text

Sentiment Analysis API Use- Cases

Sentiment Analysis understands the social sentiment of your brand, product, or service while monitoring online conversations. It is contextual mining of a text which identifies and extracts subjective information in the source material.

Why Sentiment Analysis is Important?

Multinational companies nowadays are trying to know the hidden sentiment of texts to understand their customers. Earlier, businesses relied on surveys, workshops to gain insight into their customer’s opinions and feelings, but today with the help of artificial intelligence, MNCs are able to harness meaning from text and see into opinions of customers. Sentiment analysis API, with the help of machine learning techniques, extract the overall sentiment behind the open-ended or unstructured data.

How Does Sentiment Analysis APIs Work?

The following points describe how Sentiment Analysis APIs works

• The input data goes through pre- processing where it gets filtered. The punctuations and links are removed and the data becomes more refined and relevant for the system.
• After pre- processing, each word in the text is converted to their numeric representation , which are then fed to neural architecture .
• These numeric representation  are then passed through a series of recurrent layers and then to  final output (sentiment). The output received is binary that corresponds to positive and negative context.
• This output is then compared with the actual human tagged labels and then the error is calculated which is finally used to optimize the neural network through backpropagation using SGD. This process goes on until the network is optimized satisfactorily.

CONCLUSION

ParallelDots’ Sentiment Analysis API is trained on a large and varied cluster of open-ended data to give out optimum results. To see how our API works, try the free demo now.