#Sentiment Analysis

Explore core NLP concepts and models such as Word2Vec and LDA, alongside practical uses like sentiment analysis. See how NLP improves textual comprehension in tasks like spam detection and document classification. This GitHub-updated open-source resource provides flexibility and timely insights in the rapidly advancing NLP field.

The AJAX-based movie recommendation system offers personalized movie suggestions, utilizing TMDB and IMDb data. It integrates sentiment analysis from IMDb reviews, using web scraping and natural language processing tools like BeautifulSoup to analyze user reviews. The system functions seamlessly despite typographical errors and highlights efficient data handling along with real-time sentiment insights. However, it notes language support limitation and memory constraints managed effectively.

Flair is a state-of-the-art natural language processing library offering tools for tasks like named entity recognition, sentiment analysis, and part-of-speech tagging. It is developed by Humboldt University of Berlin and supports a wide range of languages, with a particular focus on biomedical text processing. The library simplifies the use and combination of different embeddings with user-friendly interfaces and is built on PyTorch, which allows easy training of custom models. Comprehensive tutorials enable users to efficiently explore and deploy high-performance NLP models, with accessibility via platforms such as Hugging Face.

Understand a range of methods used in Chinese sentiment analysis, including techniques based on sentiment dictionaries, traditional machine learning such as Bayes, and advanced deep learning with models like ALBERT. The project explores both unsupervised and supervised approaches for text data sentiment classification, emphasizing the integration of unknown tokens like emojis to improve sentiment semantic analysis. This overview presents distinctive attributes and practical implementations of each method.

Pysentimiento is an open-source Python library tailored for sentiment analysis and other social NLP tasks using transformer models. It supports several languages, such as Spanish, English, Italian, and Portuguese, in tasks including hate speech and emotion detection, irony detection, as well as NER & POS tagging. Installable via pip, it provides quick sentiment predictions and emotion analysis. It also features preprocessing tools for social media content. Note models are based on third-party datasets and are suitable for non-commercial use. Access comprehensive guides and examples on Colab to make the most of its functionality in analyzing multilingual social media content.

This series of tutorials offers a detailed guide on sequence classification for sentiment analysis utilizing PyTorch, covering Neural Bag of Words, Recurrent Neural Networks, Convolutional Neural Networks, and BERT transformers. It begins with foundational models and gradually advances in complexity and precision for movie review sentiment prediction. Instructions for environment setup and essential resources are provided, making it suitable for both newcomers and experienced practitioners of sentiment analysis in Python.

PHP Text Analysis is a reliable library offering Information Retrieval and NLP tools specifically for PHP. It includes features such as document classification, sentiment analysis, and frequency analysis. Additionally, it offers support for tokenization, stemming, n-gram generation, and keyword extraction with the Rake algorithm. Customization options for tokenizers and stemmers allow developers to adapt the library to their needs. The accompanying documentation provides useful guidance for implementation, aiding developers in adding robust text analysis capabilities to their PHP projects.

This web app uses machine learning and sentiment analysis for forecasting stock market trends over the next seven days. Incorporating ARIMA, LSTM, and Linear Regression algorithms, it predicts stock prices while Twitter sentiment analysis offers recommendations on market movements. It provides real-time stock data, news, and currency conversion tools for NASDAQ and NSE. Admin users can manage users and emails. Developed with Python, Django, and React, it's designed for stock market analysis.