Case Studies

 

 

Case Study 1: Analyzing Campaign Rhetoric (Political Science / Research) (social media analyse)

• The User: A political science student researching communication strategies in a recent election.
• The Challenge: To objectively compare the emotional tone and key themes used in the announcement speeches of two opposing candidates.
• The Data: Two .txt files, each containing the full transcript of a candidate's campaign announcement speech.
• How the Software Helps:
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  • Sentiment Analysis: Upload each speech separately. The software provides an overall sentiment score (positive/negative/neutral) for each, offering a high-level comparison of the prevailing tone. The student can also select specific paragraphs addressing similar topics (e.g., the economy) in both speeches to compare sentiment on key issues.
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  • Word Occurrence Count: Analyze each transcript to identify the most frequently used words. This helps pinpoint key themes, buzzwords, and potentially loaded terms emphasized by each candidate (e.g., "freedom," "crisis," "jobs," "taxes").
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  • POS Tag Distribution: Compare the relative frequency of verbs (action-oriented language) vs. adjectives (descriptive language) to understand subtle differences in rhetorical style.
• The Outcome: The student gains quantifiable data to support their analysis of how each candidate used emotional language and framed key issues, moving beyond subjective interpretation. They identify distinct thematic focuses and stylistic approaches in their research paper.

Case Study 2: Refining Character Voice (Writing / Editing)

• The User: A novelist working on a multi-perspective story.
• The Challenge: Ensuring two main characters have distinct and consistent voices throughout the manuscript, reflecting their different personalities and backgrounds.
• The Data: A .txt file containing the entire novel draft.
• How the Software Helps:
  • Word Occurrence Count: The writer isolates chapters or sections narrated by each character. By running word frequency analysis on these separate sections, they can identify unintentionally shared "pet words" or check if vocabulary complexity matches the character's profile.
  • POS Tag Distribution: Analyzing the grammatical structure of sentences within each character's sections helps verify stylistic differences (e.g., one character uses shorter, simpler sentences with more nouns, while another uses complex sentences with more subordinate clauses and adverbs).
  • Sentiment Analysis: Selecting dialogue passages for each character allows the writer to check if the expressed sentiment aligns with the character's intended emotional state in specific scenes.
• The Outcome: The writer identifies areas where character voices were blurring and makes targeted revisions. The analysis helps ensure each character's dialogue and narration feel unique and authentic, strengthening the overall narrative.

Case Study 3: Expediting Legal Document Review (Legal)

• The User: A paralegal assisting with discovery for a contract dispute case.
• The Challenge: Quickly sift through hundreds of scanned contracts (converted to .txt or data extracted to .xlsx) to identify all instances of specific clauses or terms relevant to the case (e.g., "termination for convenience," "liability cap," specific party names).
• The Data: A folder of .txt files or a single .xlsx file containing text extracted from numerous contracts.
• How the Software Helps:
  • Word Occurrence Count: Uploading the files allows the paralegal to search for the exact frequency and location of critical legal terms or phrases across the entire document set. This rapidly flags potentially relevant documents for closer manual review.
  • (Optional) Morphological Analysis: If needing to find variations of a term (e.g., "terminate," "terminated," "termination"), this analysis can help understand related word forms present in the text.
• The Outcome: The software significantly reduces the time spent manually scanning documents. The paralegal quickly generates a list of high-priority contracts containing the specified terms, streamlining the discovery process and potentially saving hours of work.

Case Study 4: Analyzing Customer Feedback (Data Analysis / Business)

• The User: A data analyst for a software company.
• The Challenge: To analyze thousands of open-ended responses from a recent customer satisfaction survey to identify key themes, product issues, and overall sentiment.
• The Data: An .xlsx file where one column contains the raw text feedback from customers.
• How the Software Helps:
  • Sentiment Analysis: Evaluate the sentiment score for each feedback entry, allowing for quantitative tracking of satisfaction levels and identification of highly negative or positive comments. ¹  
    1. What is Customer Sentiment Score & How to Measure It?

    

    userpilot.com
  • Word Occurrence Count: Identify the most frequently mentioned nouns and adjectives (e.g., "bug," "slow," "interface," "easy," "helpful," feature names) to quickly surface common pain points and praised aspects.
  • Basic Harmfulness Assessment: Quickly screen for and potentially flag overly toxic or non-constructive feedback for separate review or filtering.
• The Outcome: The analyst efficiently processes large volumes of qualitative data, identifying trends in customer sentiment, pinpointing specific features causing frustration or delight, and providing actionable insights to the product development team.

Case Study 5: Investigating Phonetic Patterns (Linguistics / Research)

• The User: A linguistics researcher studying phonetic differences in online forum discussions compared to formal essays.
• The Challenge: To determine if informal online text exhibits a measurably different ratio of vowels to consonants compared to formal academic writing.
• The Data: Two .txt files: one containing scraped (and anonymized) forum posts on a specific topic, the other containing academic essays on the same topic.
• How the Software Helps:
  • Vowel/Consonant Analysis: The researcher uploads each file and analyzes representative samples (phrases/sentences) from both datasets. The software provides the vowel/consonant count and ratio for the selected text segments.
  • (Optional) POS Tag Distribution: Comparing the distribution of grammatical categories might also reveal structural differences accompanying the phonetic ones.
• The Outcome: The researcher obtains quantitative data comparing the phonetic composition of the two text types, which can be used to support or refute hypotheses about the characteristics of informal digital language versus formal written language.

These case studies illustrate how different features of your software can provide valuable, concrete insights for various users dealing with diverse types of text data.