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Top 25 NLP Projects | Natural Language Processing Projects

DAY 11 DESIGN

What is Natural Language Processing

Natural Language Processing (NLP) refers to a branch of computer science and artificial intelligence concern with giving computers the ability to understand text and spoken words in the same way as human can. It formulates to build software that generates and comprehends natural languages to have conversations of through programming.

 

Top 25 NLP Projects

  1. Hotel review rating classification using NLP
  2. Election Results prediction based on Twitter data
  3. Arabic Natural Language Processing
  4. Hashtag Clustering using NLP | Machine Learning
  5. Text Summarization using NLP I Machine Learning
  6. Rating Prediction using Machine Learning

 

  1. Hotel review rating classification using NLP

The technique that allows machines to read and understand through human emotions and extract useful insights for many businesses to grow and develop in the field. Hotel reviews collected from the guests can be classified into three subclasses i.e., positive, negative, or neutral and therefore it can analyze the sentiment of the customer.

 

  1. Election Results prediction based on Twitter data

Sentiment analysis methods have been used to improve the predictive results of counting methods. It significantly in relation to the observation period, the data collection and cleansing methods, and the performance evaluation strategy.

 

  1. Arabic Natural Language Processing

This oversight by developing tools and techniques that deliver state-of-the-art performance in a variety of language processing tasks. Machine translation is most active area of research, but also worked on statistical parsing and part-of-speech tagging.

 

  1. Hashtag Clustering using NLP | Machine Learning

Hashtag prediction is the task of mapping text to its accompanying hashtags. In this process a novel model for hashtag prediction and show this task a useful surrogate for learning good representations of text. This hashtag based detailed query show the result as whether it will be positive or negative and random forest algorithm.

 

  1. Text Summarization using NLP I Machine Learning

The method of extracting these summaries from the original huge text without losing vital information. It is to identify the important sections, interpret the context and reproduce in a new way. This ensures that the core information is conveyed through shortest text possible.

  1. Rating Prediction using Machine Learning

The review text content analysis and uses the principles of natural language process. This method insights can be drawn from the relationship between costumers and items. This is based on systems, specifically on collaborative filtering, and focuses on the reviewer’s point of view.

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