Natural Language Processing & Sentiment Analysis
Understand your customer’s needs To improve your product
Twitter has 500 million daily tweets and Instagram has 800 million monthly active users, with 90 percent of them being under the age of 35. Reddit’s users post 2.8 million comments every day, while 68 percent of Americans use Facebook.
Every second, a stunning quantity of data is created, making it increasingly difficult to extract meaningful insights from the muck. Is there a way to obtain a sense of it for your speciality in real time? With the rise of voice interfaces and chatbots, NLP is one of the most important technologies of the 4th Industrial Revolution and become a popular area of AI. There’s a fast-growing collection of useful applications derived from the NLP field. They range from simple to complex. Below are a few of them:
- Search, spell checking, keyword search, finding synonyms, complex questions answering extracting information from websites such as: products, price, dates, locations, people, or names
- Machine translation (i.e., Google Translate), speech recognition, personal assistants (think about Amazon Alexa, Apple Siri, Facebook M, Google Assistant or Microsoft Cortana)
- Chatbots/dialogue agents for customer support, controlling devices, ordering goods matching online advertisements, sentiment analysis for marketing or finance/trading
- Identifying financials risks or fraud
Natural Language Processing (NLP) is a branch of computer science that combines artificial intelligence, linguistics, and computer science. The objective is for computers to interpret or “understand” natural language in order to do human-like activities such as language translation and question answering.
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How are words/sentences represented by NLP?
The genius behind NLP is a concept called word embedding. Word embeddings are representations of words as vectors, learned by exploiting vast amounts of text. Each word is mapped to one vector and the vector values are learned in a way that resembles an artificial neural network. Each word is represented by a real-valued vector with often tens or hundreds of dimensions. Here, a word vector is a row of real valued numbers where each number is a dimension of the word’s meaning and where semantically similar words have similar vectors. i.e., Queen and Princess would be closer vectors.