Natural Language Processing

This module contains methods to accomplish several tasks related to the field of natural language processing (NLP) such as:

P R E P R O C E S S I N G:

Although there are many high-level API available, performing the text cleaning manually can give some advantages in regards of customization.

• Loading the data and selecting relevant parts
• Removing punctuation
• Removing words shorter than a choosen length
• Replace numbers with their word-based equivalent
• Removing stopwords
• Lemmatization
• Tokenization

N L P:

• Sentiment Analysis
• Part-of-Speech-Tagging (POS-Tagging)
• Named-Entity-Recognition (NER)
• TF-IDF Scoring
• Cosine Similarity
• MinHashing
• WordEmbedding
• Latent Semantic Analysis (LSA)