Natural Language Processing: Text Mining

What is Natural Language Processing:

Introduction:

We humans talk with people in the form of words, sentences, phrases by using natural languages in which both the people are comfortable in it. Human can communicate with people in different languages but machine don’t understand our natural language. It doesn’t know words, sentences, phrases etc. This information is in unstructured format. Computers can’t understand this unstructured information not even interprets this information.   So the Natural Language Process (NLP) is used to extract the meaningful information, meaningful patterns from this unstructured data. 

What is Natural Language Processing?

NLP is a combination of the areas such as Neuroscience, Linguistic, Mathematics, Computer Science and statistics. Neuroscience is used for sentiment analysis.

 If you want to predict on consumer sentiments for a product like washing machine on Amazon. You can get information about what are the consumer sentiments for that product. Then you can come up with sentiment analysis like positive, negative or neutral sentiments with that product. What the people are talking about these products.

What is Text Mining?

Text mining is a computational methods and techniques which is used to extract high quality information from the text i.e. unstructured data.

A computational approach is nothing but is the discovery of new, previously unknown information which is automated extracted from unstructured texts.

Text Mining is Useful,

Large amount of information that is coming from Internet it is unstructured. This information is,

Books, Pdf, Financial and business reports, News articles, Blog post, Wiki answers

Social media posts, various types business documents, various review reports etc.

This huge unstructured data is estimated as 80% from all available data on internet. So we should apply the text mining to extract the meaningful information from this unstructured data. We can summarize our data with different patterns. We apply some automated tools to extract the useful information from unstructured data.

We can preprocess our text data by applying text mining techniques.

Applications of NLP: 

1. Documents classification: like documents belongs to HR department, Finance department or marketing department. 

2. Clustering/ organizing documents: suppose documents about sports, politics or social media so we can organize documents based on characteristics of each documents or word used they have been using in documents.

3. Documents Summarizations: If you want to summarize particular documents.

4.  Visualization of documents: We can visualize have lot of vocabulary in our documents.

5. Making Predictions: Stock market prediction of a particular company based on analysis of news articles and research reports.

6. Content Based Recommended Systems: If you are interested in Indian cricket teams then you can find out which articles are similar to your study and you can select by using content based recommender systems.

7. Vistual Assistants:  You can develop assistants like  Alexa, Siri,  Cortana etc.

8. Chat bots: Chat bots help you to understand what your requirements are. 

9. Sentiment 

key steps in Natural Language Processing (NLP), summarized concisely:

1. Text Preprocessing: Clean the raw text by removing punctuation, stop words, and special characters. Convert text to lowercase and normalize using techniques like stemming or lemmatization.

2. Tokenization: Split the text into smaller units, such as words or sentences, for easier analysis. This step helps break down the data into manageable pieces.

3. Removing Stop Words: Filter out common words (like "is," "the," "and") that carry little meaning. This helps reduce noise and focus on more relevant words.

4. Text Vectorization: Convert text into numerical representations using techniques like Bag of Words (BoW), TF-IDF, or word embeddings (Word2Vec, GloVe). This step is essential for feeding the text data into machine learning models.

5. Feature Engineering: Create additional features such as word count, sentiment scores, or n-grams. These features help improve the model's understanding of the text data.

6. Modeling: Use machine learning algorithms (SVM, Naive Bayes) or deep learning models (LSTM, BERT) to build predictive models. These models are trained on the vectorized text data.

7. Evaluation: Assess the model’s performance using metrics like accuracy, precision, recall, or F1-score. This ensures that the model generalizes well to unseen data.

8. Tuning and Optimization: Fine-tune hyperparameters, adjust features, or improve preprocessing steps to enhance model performance. This helps achieve better accuracy or efficiency.

9. Deployment: Integrate the model into real-world applications, such as chatbots, sentiment analysis tools, or recommendation systems. This step brings the model into practical use.

10. Monitoring and Updating: Continuously monitor the model’s performance over time and retrain as necessary. This ensures the model stays relevant and accurate as new data becomes available.

Download dataset(apple.txt)

Go to Jupyter Notebook: Text Preprocessing and Mining👇👇👇