How to Mine Text from WPS Documents Using Add‑Ons

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.

Your initial action should be saving the document in a structure suitable for computational analysis.

WPS documents are commonly exported as TXT, DOCX, or PDF formats.

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.

When working with tabular content, export tables directly from WPS Spreadsheets into CSV format for efficient numerical and textual analysis.

You can leverage Python’s PyPDF2 and python-docx libraries to parse text from exported PDF and DOCX files.

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

For example, python-docx can read all paragraphs and tables from a WPS Writer document saved as DOCX, giving you access to the raw text in a structured way.

Once text is extracted, preprocessing becomes the critical next step.

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

Both NLTK and spaCy are widely used for text normalization, tokenization, and linguistic preprocessing.

You may also want to handle special characters or non-English text using Unicode normalization if your documents contain multilingual content.

The cleaned corpus is now ready for pattern discovery and insight generation.

TF-IDF highlights keywords that stand out within your document compared to a larger corpus.

Visualizing word frequency through word clouds helps quickly identify recurring concepts and central topics.

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

For multi-document analysis, LDA reveals thematic clusters that aren’t immediately obvious, helping structure unstructured text corpora.

Integrating plugins with WPS can significantly reduce manual steps in the mining pipeline.

While WPS does not have an official marketplace for text mining tools, some users have created custom macros using VBA (Visual Basic for Applications) to extract text and send it to external analysis scripts.

Once configured, these scripts initiate export and analysis workflows without user intervention.

By integrating WPS Cloud with cloud-based NLP services via automation tools, you achieve hands-free, scalable text analysis.

Another practical approach is to use desktop applications that support text mining and can open WPS files indirectly.

Applications such as AntConc and Weka provide native support for text mining tasks like keyword spotting, collocation analysis, and concordance generation.

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

Always verify that third-party tools and cloud platforms meet your institution’s security and compliance standards.

Local processing minimizes exposure and ensures full control over your data’s confidentiality.

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.

Cross-check your findings with manual reading of the original documents to ensure that automated insights accurately reflect the intended meaning.

Leverage WPS as a content hub and fuse it with analytical tools to unlock latent trends, emotional tones, and thematic clusters buried in everyday documents.