非结构化数据分析

Non-structured Data Analysis: Techniques and Tools

With the advent of big data, non-structured data has emerged as a critical area of analysis for businesses across all verticals. The problem with non-structured data is that it is not organized in a predefined manner and the data doesn't fit well with preconceived models. Thus, understanding and analyzing non-structured data could be quite challenging for business analysts and data scientists alike. However, there are various tools and techniques available to aid the analysis of non-structured data.

Here, we'll delve into different areas of non-structured data analysis, including natural language processing (NLP), text mining, and data visualization. We’ll analyze some of the tools and libraries that can aid in each of these areas.

Natural Language Processing (NLP)

NLP is a subfield of artificial intelligence that focuses on the interaction between human language and machine language. NLP helps to extract meaningful information from unstructured and unlabeled data. There are different NLP techniques used to pre-process data such as stemming, tokenization, lemmatization, named entity recognition, and sentiment analysis.

Stemming: Stemming is the process of reducing a word to its root form. For instance, stemming the word "lightning" would result in "light."

Tokenization: Tokenization is the process of splitting text into individual tokens (words or phrases).

Lemmatization: Lemmatization is the process of grouping different word forms together based on their root form. For example, the verb forms "run", "ran", and "running" would be lemmatized to "run".

Named Entity Recognition: Named Entity Recognition (NER) is the extraction of specific entities such as names, locations, and dates from textual data.

Sentiment Analysis: Sentiment analysis is the process of determining the sentiment (positive, negative, or neutral) in a piece of text.

Libraries such as Natural Language Toolkit (NLTK), spaCy, and TextBlob can be used for NLP. Here is an example of NLP code:

from nltk.tokenize import word_tokenize
from nltk.stem.porter import PorterStemmer

porter = PorterStemmer()
text = "The quick brown fox jumped over the lazy dog."
tokens = word_tokenize(text)
stemmed_tokens = [porter.stem(token) for token in tokens]
print(stemmed_tokens)

Text Mining

Text mining is the process of analyzing unstructured text data to extract meaningful relationships and patterns. Text mining techniques include clustering, topic modeling, and classification.

Clustering: Clustering is a machine learning technique where a set of similar data points are grouped together. Clustering algorithms such as k-means and hierarchical clustering can be used for clustering textual data.

Topic Modeling: Topic modeling is the process of discovering hidden topics in a corpus of textual data.

Classification: Classification is the process of assigning categories to unstructured textual data. Classification algorithms such as Naive