Question

Tasked to develop small project based on Natural Language processing. You are requested to see various kinds of LP's applications you can pick up any one application and make small project.

Information and materials:

You may use information from any source, however, remember that just because there is a lot of information on the internet, does not mean that it is valuable.

Try to use sources such as: credible industry sources found online, library resources, etc.

You are becoming the class experts on your assigned type of media, so make sure your information is credible.

The assignment must consist of

Information on 'Basics / introduction part, how it works?

Dataset related information

Code with appropriate comment for easy understanding

Required documents for submission: jupypter notebook file

Also please attached dataset link if it used in program.

(Except IMDB movie and bbc news Dataset try with different data set also try different np application

Machine Translation

Market Intelligence

Auto-Correct

Intent Classification

Urgency Detection

Speech Recognition)

Answer 1

Natural Language Processing (NLP) is a branch of AI that focuses on computers' interaction with human language. An example application of NLP is sentiment analysis, which involves classifying the sentiment expressed in text. To develop a sentiment analysis project, you need to collect and preprocess a dataset of customer reviews, vectorize the text data, train a machine learning model, and evaluate its performance.

Step-by-step explanation:

Introduction to Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves analyzing, understanding, and generating human language, enabling computers to process and respond to text or speech data.

Example Application: Sentiment Analysis

One application of NLP is sentiment analysis, which involves determining the sentiment or emotion expressed in a piece of text. For a small project, you can develop a sentiment analysis system that analyzes customer reviews to classify them as positive, negative, or neutral.

Steps to Develop a Sentiment Analysis Project:

1. Collect a dataset of customer reviews, labeled with their corresponding sentiments.
2. Preprocess the text data by removing stopwords, converting text to lowercase, and handling punctuation.
3. Vectorize the text data by transforming it into numerical features using techniques like bag-of-words or word embeddings.
4. Select a machine learning algorithm or a pre-trained model and train it on the labeled dataset.
5. Evaluate the model's performance using metrics like accuracy, precision, recall, and F1 score.
6. Use the trained model to predict the sentiment of new customer reviews.

Remember to provide code with appropriate comments in a Jupyter Notebook file and include the dataset link if used in the program. Good luck with your project!