Sentiment Analysis

Sentiment Analysis is the process of using computational techniques to determine and quantify the emotional tone, opinion, or sentiment expressed in text data. It is widely used in natural language processing (NLP) for applications such as customer feedback analysis, social media monitoring, product reviews, and market research. Python libraries like TextBlob, NLTK, and Hugging Face Transformers provide tools for performing sentiment analysis on text, allowing users to classify text as positive, negative, or neutral, and extract subjective information efficiently.

Features of Sentiment Analysis:

• Supports polarity detection (positive, negative, neutral) of text data.
• Provides subjectivity scoring to identify opinionated versus factual content.
• Compatible with popular Python NLP libraries like TextBlob, NLTK, and Hugging Face.
• Can be applied to a wide range of text sources, including social media, reviews, and articles.
• Enables batch processing and analysis of large datasets.
• Lightweight and easy to integrate into existing Python workflows.
• Open-source and actively maintained by the NLP research community.
• Provides simple APIs for quick sentiment scoring and text preprocessing.

To verify the working of Sentiment Analysis in your shell:

$ sudo su
$ sudo apt update
$ cd /opt/sentiment-analysis
$ source venv/bin/activate
$ python3 sentiment_analysis.py

Disclaimer: Sentiment Analysis scripts and libraries are open-source tools provided for research and educational purposes. They are not affiliated with, endorsed by, or sponsored by any specific organization. The tools are provided "as is," without any warranty, express or implied. Users assume full responsibility for their usage, and the developers and contributors are not liable for any damages, losses, or consequences resulting from the use of these tools. Users should review and comply with licensing terms and applicable regulations when using sentiment analysis libraries and scripts.