πŸ§ͺ Use Cases

This page showcases practical examples of how TALL can be applied to real-world text corpora. Each case follows some phases of TALL workflow β€” from data import and preprocessing to insight generation and interpretation β€” enhanced by TALL AI.

1. 🎬 BBC News

Goal
Explore themes, vocabulary, and summarize information in entertainment news articles from the BBC.

πŸ“„ Dataset

A curated collection of 386 short news stories from the Entertainment section of BBC News (in English).

πŸ” Workflow

  1. Import the dataset directly from TALL’s built-in sample collections and write a brief description of data in the TALL AI box.

bbc import

  1. Preprocess using English-specific NLP pipeline: Tokenization & PoS tagging using the right language model.

bbc tok

  1. Multi-word automatically created using the RAKE algorithm. Then, all generated multiwords are included in the dataset.

bbc multi

  1. PoS Selection, including verbs, nouns, proper nouns, adjectives, and multi-words

bbc multi

  1. Lexical Exploration visualizing vocabulary through word clouds

bbc wc

  1. Use Word in context for "million dollar baby" term and ask an to TALL AI an interpretation of results.

bbc wic

  1. Topic Modeling

    • Apply LDA (Latent Dirichlet Allocation) to detect latent topics and then ask to TALL AI a label for each topic.

    bbc tm

  2. Summarization, using TextRank to generate concise summaries of a document providing the most relevant sentence

bbc textrank

2. πŸ“š Bibliometrix Abstracts

Goal
Analyze the conceptual landscape of scientific literature that references the Bibliometrix R package.

πŸ“„ Dataset

A corpus of 444 scientific abstracts that cite Bibliometrix, enriched with metadata such as authors, publication year, and journal name. The abstracts have already been tokenized and POS tagged using tall.

πŸ” Workflow

  1. Import the .tall file. If the dataset has already been processed and exported from TALL, re-importing the .tall file will automatically restore the session and display a summary of all previously completed analytical steps.

bib import

  1. Filter the abstracts to include only article published between 2017 and 2021

bib filter

  1. Lexical and Structural Analysis performing a Co-Ward network to detect conceptual clusters and ask to TALL AI the interpretation.

bib cw

bib cwai

3. ✈️ US Airlines Tweets

Goal: Understand customer feedback and emotional tone in airline-related conversations on Twitter.

πŸ“„ Dataset

14.640 tweets mentioning major U.S. airlines, collected in February 2015. The dataset includes tweet content, airline names, and metadata such as time and location.

πŸ” Workflow

  1. Import the raw CSV file directly into TALL

air import

  1. Preprocess the corpus using a domain-specific PoS tagging model trained on social media language

air tok

  1. Tag special entities such as @mentions, #hashtags, and emojis for semantic enrichment

air tse

  1. Build an Ego Network around #fail hashtag, to identify co-occurring complaint patterns

air wic

  1. Perform Sentiment Analysis using the NRC Emotion Lexicon to detect emotional polarity and dominant sentiments (e.g., anger, trust, fear)

air pol

air polai

4. 🧾 Wikipedia Pages

Goal: Discover sub-themes and semantic structures within machine learning content.

πŸ“„ Dataset

A collection of 15 Wikipedia pages related to machine learning, retrieved directly via TALL’s import interface.

πŸ” Workflow

  1. Import Wikipedia articles from the integrated TALL module about machine learning.

wiki import

  1. Generate multi-word expressions using the RAKE algorithm to extract domain-relevant collocations.

wiki mw

  1. Explore lexical insights, including document and sentence length, word frequency distribution, and keyword clouds.

wiki overview

  1. Build a co-word network to visualize thematic associations, with TALL AI support for identifying latent sub-themes in machine learning discourse.

wiki cw

  1. Apply topic modeling (LDA) to extract six key topics and their representative terms, enriched by TALL AI interpretation and summary.

wiki tm

wiki tmai

✨ Your Own Use Case

TALL is flexible and scalable for use in:

  • Social science research
  • Digital humanities
  • Public policy analysis
  • Journalism and media studies
  • Education and learning analytics
  • Marketing and brand sentiment monitoring

Have a use case to share? Contribute on GitHub or get in touch via the About page.