RobinMillford / NLP-Based-Analyzing-and-Processing-Text-Data

The objective of this project was to analyze and process text data from a dataset containing articles obtained from various websites. The goal was to extract valuable information, perform sentiment analysis, and calculate various text metrics to gain insights from the text corpus.

Project Summary: Analyzing and Processing Text Data

Objective: The objective of this project was to analyze and process text data from a dataset containing articles obtained from various websites. The goal was to extract valuable information, perform sentiment analysis, and calculate various text metrics to gain insights from the text corpus.

Key Steps and Actions Taken:

1. Data Collection and Preprocessing:

   • Data was obtained from a dataset containing article URLs.
   • Each URL was accessed, and the text content of the articles was extracted using web scraping techniques.
   • The extracted text was cleaned by removing special characters, punctuation, and stop words to prepare it for analysis.

2. Text Tokenization:

   • The cleaned text was tokenized into sentences to analyze each sentence individually.
   • The NLTK library was used for tokenization, and the text was split into sentences using regex patterns.

3. Sentiment Analysis:

   • Sentiment analysis was conducted to determine the emotional tone of each sentence.
   • Positive and negative sentiment dictionaries were used to calculate positive and negative scores for each sentence.
   • The polarity score was computed, representing the overall sentiment of each sentence.

4. Text Metrics Calculation:

   • Various text metrics were calculated for each sentence, including average sentence length, percentage of complex words, Fog Index, average number of words per sentence, word count, syllables per word, personal pronouns count, and average word length.

5. Data Analysis and Visualization:

   • The calculated metrics and sentiment scores were analyzed to gain insights into the text corpus.
   • Visualizations, such as histograms or word clouds, could be generated to illustrate the findings.

6. Data Export:

   • The processed data, including the calculated metrics and sentiment scores, was organized into a structured DataFrame.
   • The final DataFrame was saved as a CSV file for further analysis or reporting.

Tools and Libraries Used:

• Python
• Pandas: Data manipulation and analysis
• Requests and BeautifulSoup: Web scraping
• NLTK (Natural Language Toolkit): Text preprocessing, tokenization, and sentiment analysis

Results and Insights:

• The sentiment analysis provided insights into the overall emotional tone of the text corpus.
• Text metrics offered quantitative information about the complexity and structure of sentences.
• These insights can be used for various purposes, such as content evaluation, readability assessment, or further natural language processing tasks.

Future Directions:

• The processed data and insights can be used for more advanced natural language processing tasks, such as text classification or topic modeling.
• Additional features or metrics could be calculated to further characterize the text data.
• The project could be extended to perform analysis on a larger and more diverse dataset for broader insights.

Conclusion: This project successfully processed and analyzed a text corpus, providing valuable insights into the content's sentiment and structure. It demonstrates the use of Python and various libraries for text analysis, laying the foundation for more advanced text analytics and NLP projects.