Sentiment analysis module using AFINN-111
AFINN-based sentiment analysis for Node.js
Sentiment Analysis
Multi language AFINN-based sentiment analysis for Node.js
General natural language (tokenizing, stemming (English, Russian, Spanish), part-of-speech tagging, sentiment analysis, classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.
Sentiment Analysis
AFINN-based sentiment analysis for Node.js
Reusable sentiment analysis library with Gemini & OpenAI support
Sentiment Analysis module
Multilanguage AFINN-based sentiment analysis for Node.js
A Node-RED node that uses the AFINN-165 wordlists for sentiment analysis of words.
Sentiment analysis for hubot
Sentiment Analysis Ai MCP server. Tools: analyze sentiment, batch analyze, compare sentiments. Built by MEOK AI Labs.
Multilanguage AFINN-based sentiment analysis for Node.js
Very compact and simple to use sentiment analysis lib
Collection of functions for sentiment analysis
n8n community node for sentiment analysis using Sentor AI API
Web Page Inspection Tool. Sentiment Analysis, Keyword Extraction, Named Entity Recognition & Spell Check
Chinese sentiment analysis for Node
nlp.js from axa-group in typescript 🚀. NLP library for building bots 🤖, with entity extraction, sentiment analysis, automatic language identification, and more.
Sentiment analysis is the process of detecting positive or negative sentiment in text
Salient is a natural language processing and sentiment analysis library
Sentimex SDK for building sentiment analysis applications with React
A powerful text analytics library that provides easy integration with AWS Comprehend and Azure Text Analytics for language detection, sentiment analysis, entity recognition, and key phrase extraction.
A faster Rust version from the original Python VaderSentiment analysis tool.
CoreML inference engine for Candle tensors - provides Apple CoreML/ANE integration with real tokenization, safety fixes, and model calibration awareness
NLP Cloud serves high performance pre-trained and custom models for NER, sentiment-analysis, classification, summarization, question answering, and POS tagging, ready for production, served through a REST API. More details here: https://nlpcloud.io. Documentation: https://docs.nlpcloud.io
TrustformeRS - Rust port of Hugging Face Transformers
Bindings for Rust from the original Python VaderSentiment analysis tool. Forked for use with qsv.
Client for the SentimentAnalysis API
A simple sentiment analysis gem
Sentiment analysis with ruby.
Provides natural language understanding technologies, such as sentiment analysis, entity recognition, entity sentiment analysis, and other text annotations. Note that google-cloud-language-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-language instead. See the readme for more details.
Provides natural language understanding technologies, such as sentiment analysis, entity recognition, entity sentiment analysis, and other text annotations. Note that google-cloud-language-v1beta2 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-language instead. See the readme for more details.
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.
A simple and extensible sentiment analysis gem.
A very simple to use sentiment analysis tool
Sentiment analysis in Ruby. Languages supported: English 🇬🇧, Danish 🇩🇰
sentiment-al is Sentiment Analysis Service that allow the analysis of text throught SENTIM-API.
Sentiment analysis for the German language
Provides natural language understanding technologies, such as sentiment analysis, entity recognition, entity sentiment analysis, and other text annotations, to developers. Note that google-cloud-language-v2 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-language instead. See the readme for more details.
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