#sentiment analysis

LM-PPL: An Efficient Tool designed to calculate text perplexity using pre-trained language models such as GPT, BERT, and T5. It aids in assessing text fluency by computing ordinary perplexity for recurrent models and decoder perplexity for encoder-decoder models, while utilizing pseudo-perplexity for masked models. Suitable for a range of applications like sentiment analysis, LM-PPL helps select texts with lower perplexity, ensuring better model predictions. Installable via pip, it offers a user-friendly way to leverage popular models for varied text evaluation needs.

HarvestText is a text mining toolkit that specializes in unsupervised text analysis and domain knowledge integration to efficiently process domain-specific texts. It supports tasks such as entity linking, sentiment analysis, and key phrase extraction. Suitable for various text preprocessing and exploratory analyses in fields like literature, web content, and more. The tool is Python 3.6+ compatible and aids in named entity recognition and dependency parsing without complex setups.

This open-source project leverages PyTorch, CUDA, and Python 3.5 for sentiment analysis by generating and analyzing sentiments in reviews. It supports model implementation, visualization, and retraining with adjustable parameters including sequence length, batch size, and RNN setup, providing a flexible framework for developers to explore sentiment analysis.

PyTextClassifier is an open-source Python library designed for text classification and clustering applications, incorporating a diverse range of algorithms including Logistic Regression, Random Forest, Decision Tree, and advanced deep learning models like BERT and FastText. It supports sentiment analysis, risk classification, and other complex classification tasks such as binary, multi-class, multi-label, and hierarchical classifications. The library offers straightforward installation and usage for efficient model training, evaluation, and deployment, ensuring high performance and clarity with an emphasis on modular design and ease of use.

The 'similarity' toolkit written in Java provides robust text similarity computation and sentiment analysis capabilities. This toolkit includes various algorithms for comparing words, phrases, sentences, and paragraphs, with notable methods like Cilin similarity and cosine similarity. It is designed for natural language processing, offering customizable modules, low coupling, and lazy model loading. Additionally, it supports semantic analysis using concept trees and synonym suggestions with word2vec models. Integration with Maven and Gradle is straightforward, making it a valuable resource for developers aiming for efficient and scalable text analysis solutions.

NewsNerd HackerBot provides access to top Hacker News stories, sentiment analysis on comments, and linked article insights. Users can filter by keywords for a detailed understanding. Accessible via Streamlit Cloud or locally with simple installation.