Text Mining WPS Files with Third‑Party Tools > 자유게시판

Performing text mining on WPS documents requires a combination of tools and techniques since WPS Office does not natively support advanced text analysis features like those found in dedicated data science platforms.

The first step is to export your WPS document into a format compatible with text mining tools.

For compatibility, choose among TXT, DOCX, or PDF as your primary export options.

For the best results, saving as DOCX or plain text is recommended because these formats preserve the structure of the text without introducing formatting noise that could interfere with analysis.

CSV is the most reliable format for extracting structured text from WPS Spreadsheets when performing column-based analysis.

Text extraction becomes straightforward using tools like PyPDF2 (for PDFs) and python-docx (for DOCX documents).

These modules enable automated reading of document content for downstream processing.

Using python-docx, you can extract full document content—including headers, footers, and tables—in a hierarchical format.

Before analysis, the extracted text must be cleaned and normalized.

You should normalize case, discard symbols and numerals, remove stopwords, and apply morphological reduction techniques like stemming or lemmatization.

Python’s NLTK and spaCy provide comprehensive functionalities for cleaning and structuring textual data.

If your files include accented characters, non-Latin scripts, or mixed languages, apply Unicode normalization to ensure consistency.

With the cleaned text ready, you can begin applying text mining techniques.

Use TF-IDF to rank terms by importance, revealing words that are distinctive to your specific file.

A word cloud transforms text data into an intuitive graphical format, emphasizing the most frequent terms.

Sentiment analysis with VADER (for social text) or TextBlob (for general language) reveals underlying emotional direction in your content.

LDA can detect latent topics in a collection of documents, making it ideal for analyzing batches of WPS reports, memos, or minutes.

Some users enhance WPS with add-ons that bridge document content to external analysis tools.

Many power users rely on VBA macros to connect WPS documents with Python, R, or cloud APIs for seamless analysis.

These VBA tools turn WPS into a launchpad for automated text mining processes.

Platforms like Zapier or Power Automate can trigger API calls whenever a new WPS file is uploaded, bypassing manual export.

Consider using standalone text analysis software that accepts exported WPS content as input.

These desktop tools are especially valued for their rich, code-free interfaces for textual exploration.

They empower users without coding experience to conduct rigorous, publication-ready text analysis.

When working with sensitive or confidential documents, ensure that any external tools or cloud services you use comply with your organization’s data privacy policies.

To enhance security, process files offline using local software instead of cloud-based APIs.

Never assume automated outputs are accurate without verification.

Text mining outputs are only as good as the quality of the input and the appropriateness of the methods used.

Human review is essential to detect misinterpretations, false positives, or contextual errors.

WPS documents, when paired with external analysis tools and careful preprocessing, become powerful repositories of actionable insights, revealing patterns invisible in raw text.