Search Results for "sentiment" - Page 3
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Showing 74 open source projects for "sentiment"
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NLP.js
An NLP library for building bots
NLP.js is an NLP library for building bots, with entity extraction, sentiment analysis, automatic language identifier, and much more. "NLP.js" is a general natural language utility for nodejs. Search the best substring of a string with less Levenshtein distance to a given pattern. Get stemmers and tokenizers for several languages. Sentiment Analysis for phrases (with negation support). Named Entity Recognition and management, multi-language support, and acceptance of similar strings, so the introduced text does not need to be exact. ... -
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SentEval
A python tool for evaluating the quality of sentence embeddings
SentEval is a standardized toolkit for evaluating sentence embeddings across a wide spectrum of downstream tasks and probing tests. It defines a simple interface—provide an encoder function from sentences to vectors—and then runs consistent training/evaluation loops for tasks like sentiment, entailment, paraphrase, and semantic textual similarity. The suite also contains linguistic probing tasks that illuminate what properties embeddings capture, such as tense, word order, or syntactic structure. Datasets are wrapped with unified preprocessing and metrics so results are comparable across papers and implementations. ... -
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NLP-Models-Tensorflow
Gathers machine learning and Tensorflow deep learning models for NLP
...The repository provides numerous examples of neural network architectures used in modern NLP research and applications, including text classification, language modeling, machine translation, and sentiment analysis. Each model implementation is designed to illustrate how common NLP architectures operate, such as recurrent neural networks, convolutional models for text processing, and transformer-style attention mechanisms. The project includes scripts for preparing datasets, training models, and evaluating performance on various text analysis tasks. ... -
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fastText
Library for fast text classification and representation
...It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ext classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool. The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. ... -
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mitigating-stereotype
Mitigating Stereotypes in Word Embedding through Sentiment Modulation
We provide the code and data for the following paper: Mitigating Stereotypes in Word Embedding through Sentiment Modulation by Huije Lee, Jin-Woo Chung, and Jong C. Park. Korea Software Congress 2018 (KSC 2018), Pyeongchang (Korea), December 2018. This repository provides a model that mitigates stereotypes in word embedding through sentiment modulation. The detailed instructions are in the readme file within the zip file. Github: https://github.com/nlpcl-lab/mitigating-stereotype -
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TEXT2DATA
Text Analytics Platform
Bring Text Analytics Platform that uses NLP (Natural Language Processing) and Machine Learning to your work environment. Extract essential information from your text documents and let Artificial Intelligence save your time. Get detailed and agile reports on your unstructured data. -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks. -
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
...The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing. -
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JSentiWordNet
A wrapper for the famous SentiWordNet, a resource for opinion mining
This project aims to provide a wrapper around the SentiWrodnet, a lexical resource for opinion mining. As defined by the authors : SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity. You can find additional information about the creation of SentiWordnet here : http://nmis.isti.cnr.it/sebastiani/Publications/LREC06.pdf sentiWordnet (avilable here : https://drive.google.com/open?id=0B0ChLbwT19XcOVZFdm5wNXA5ODg) is a text file with a specific format that saves a synset on each line. ... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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bulbea
Deep Learning based Python Library for Stock Market Prediction
...It includes utilities for splitting datasets, normalizing time series, and training models such as recurrent neural networks that can capture temporal dependencies in market behavior. The library also incorporates sentiment analysis capabilities that analyze social media data, particularly from Twitter, to estimate public sentiment toward financial assets. -
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Python Machine Learning book
The book code repository and info resource
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. This is not... -
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It gives facility of collecting tweets through Twitter Streaming API w.r.t different search criteria and to save tweets in CSV and ARFF (WEKA) file formats.
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Phrasal
Statistical phrase-based machine translation system
...Our work ranges from basic research in computational linguistics to key applications in human language technology, and covers areas such as sentence understanding, automatic question answering, machine translation, syntactic parsing and tagging, sentiment analysis. -
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VADER
Lexicon and rule-based sentiment analysis tool
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments. -
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sentiment
AFINN-based sentiment analysis for Node.js
Sentiment is a simple and lightweight sentiment analysis tool for Node.js that evaluates the polarity of text by scoring words based on positive and negative sentiment. -
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TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. -
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TexLexAn is an open source text analyser for Linux, able to estimate the readability and reading time, to classify and summarize texts. It has some learning abilities and accepts html, doc, pdf, ppt, odt and txt documents. Written in C and Python.
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Stockhub is an open source intelligence tool for stock investment. It automatically collects FA, TA, rating, sentiment from many web sites/blogs; Track top performers’ portfolios; Find the most commonly rated long/short stocks, without browser and GUI.
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Dutch sentiment analysis engine
Een module om de sentiment van een stuk Nederlandse tekst to bepalen
This application was developed by Incentro to satisfy requests by clients for a sentiment analyser for the Dutch language. It is currently in it's alpha stage and we expect to have a beta release by November 2012. If you would like to help with the development or testing of this product please contact us at +31[0]15 76 40 750 - of info {at} incentro.com. Deze applicatie is ontwikkeld door Incentro om te voldoen aan klantaanvragen voor een sentimentanalyse module voor de Nederlandse taal. ... -
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Reputation-Sentinel-OS-v3
Autonomous n8n framework for real-time brand protection & AI analysis.
...You own your data and your brand's defense. Key Capabilities: Omni-Channel: 24/7 autonomous tracking across X, YouTube, and Telegram. Deep AI Analysis: Surgical sentiment detection (sarcasm, threats, urgency) via local or cloud AI models. Incident Response: Automated rule-based workflows to mitigate damage instantly. Enterprise Power: Optimized for high-load processing without monthly fees. The ultimate self-hosted operating system for PR agencies and security-focused enterprises. Buy once, own forever. -
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t5-small
T5-Small: Lightweight text-to-text transformer for NLP tasks
...With only 60 million parameters, T5-Small is compact and suitable for fast inference or deployment in constrained environments. It was pretrained on the C4 dataset using both unsupervised denoising and supervised learning on tasks like sentiment analysis, NLI, and QA. Despite its size, it performs competitively across 24 NLP benchmarks, making it a strong candidate for prototyping and fine-tuning. T5-Small is compatible with major deep learning frameworks including PyTorch, TensorFlow, JAX, and ONNX. The model is open-source under the Apache 2.0 license and has wide support across Hugging Face's ecosystem.
