In Natural Language Processing there is a concept known as Sentiment Analysis. Given a movie review or a tweet, it can be automatically classified in categories. These categories can be user defined (positive, negative) or whichever classes you want.
In case of sentiment analysis, even humans cannot agree on 100% of the cases due to linguistic ambiguities like sarcasm. However state of the art sentiment analysis algorithms trained by thousands of real life examples perform on par with humans.
This API comes pre-trained with tens of thousands of APIs, carefully classified as positive or negative. You don't need to deal with training any model. Just call the API and go!
This API will also identify text with HTML code in it. Just send the HTML text without any worries.
“This restaurant has a lovely atmosphere and the staff is great!” → Positive
“Had an awful time at this cafe. Never seen such rude chefs.” → Negative
“The place was O.K. I really liked the food but the decoration was outdated.” → Neutral
Use Cases for Sentiment Analysis
Brands are relying heavily on analyzing social media monitoring and Sentiment Analysis API in order to measure return on investment (ROI) of their marketing efforts. Since it’s nearly impossible to evaluate every single piece of information generated by users, automated sentiment analysis tools help them save time and money. Whether it’s a movie premier or a new product redesign, they can get real time feedback from customers and analyze the results.
Investment companies also utilize Sentiment Analysis API to sniff rising stars through news search and make automated investment decisions. It would not be possible without Sentiment Analysis API to analyze all the news information instantaneously and invest in stocks before everyone else.
You can also apply thematic analysis to determine the features on which the customers are talking about the most. Sometimes it’s not just the overall sentiment of the review but the individual product features that are important to evaluate. By using thematic categorization brands can track which features are talked about the most and the sentiment toward that specific feature.