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.

Begin by converting your WPS file into a format that text mining applications can process.

You can save WPS files in plain text, DOCX, or PDF depending on your analytical needs.

To minimize interference during analysis, export your content as DOCX or plain text rather than PDF.

For datasets embedded in spreadsheets, saving as CSV ensures clean, machine-readable input for mining algorithms.

Once your document is in a suitable format, you can use Python libraries such as PyPDF2 or python-docx to extract text from PDFs or DOCX files respectively.

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.

Preprocessing typically involves lowercasing, stripping punctuation and digits, filtering out common words such as "the," "and," or "is," and reducing words to stems or lemmas.

These libraries deliver powerful, prebuilt functions to handle the majority of text cleaning tasks efficiently.

For documents with multilingual elements, Unicode normalization helps standardize character encoding and avoid parsing errors.

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

This statistical measure identifies terms with high relevance within a document while penalizing common words across multiple files.

Word clouds provide a visual representation of word frequency, making it easy to spot dominant themes.

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.

Automate your workflow by exploring WPS Office-compatible plugins or custom extensions.

Custom VBA scripts are commonly used to pull text from WPS files and trigger external mining scripts automatically.

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

You can also connect WPS Cloud to services like Google NLP or IBM Watson using Zapier or Power Automate to enable fully automated cloud mining.

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

Tools like AntConc or Weka can import plain text files and offer built-in analysis features such as collocation detection, keyword extraction, and concordance views.

Non-programmers in fields like sociology, anthropology, or literary studies often rely on these applications for deep textual insights.

For confidential materials, avoid uploading to unapproved systems and confirm data handling protocols.

Keep sensitive content within your controlled environment by running analysis tools directly on your device.

Never assume automated outputs are accurate without verification.

The accuracy of your results depends entirely on preprocessing quality and method selection.

Verify mining results by reviewing the source texts to confirm interpretation fidelity.

You can turn mundane office files into strategic data assets by integrating WPS with mining technologies and preprocessing pipelines.