This repository showcases a comprehensive approach to data extraction and sentiment analysis, providing insightful metrics for understanding textual content. The project includes tools for extracting meaningful information and assessing sentiment based on various criteria.
File Upload: Users can upload text files for analysis. Text Processing: Extracts relevant information and prepares data for sentiment analysis. Metrics Calculation: Computes various linguistic metrics for a detailed analysis.
Positive Score: Quantifies the positive sentiment in the text. Negative Score: Measures the negative sentiment expressed in the content. Polarity Score: Determines the overall polarity of the text. Subjectivity Score: Evaluates the subjectivity of the text. Average Sentence Length: Provides insights into the typical length of sentences. Percentage of Complex Words: Identifies the complexity of language used. FOG Index: A readability index measuring the complexity of the text. Average Number of Words per Sentence: Analyzes sentence structure. Complex Word Count: Counts the occurrences of complex words. Word Count: Gives the total number of words in the text. Syllables per Word: Measures the syllabic complexity of words. Personal Pronouns: Identifies the usage of personal pronouns. Average Word Length: Calculates the average length of words.
Python: Core programming language for data extraction and sentiment analysis. Natural Language Processing (NLP) Libraries: Utilized for linguistic analysis. Flask: Web framework for building the user interface. HTML/CSS: Front-end design and user interaction.