Displaying 1 to 20 from 78 results

nlp-with-ruby - Practical Natural Language Processing done in Ruby.

  •    Ruby

This curated list comprises awesome resources, libraries, information sources about computational processing of texts in human languages with the Ruby programming language. That field is often referred to as NLP, Computational Linguistics, HLT (Human Language Technology) and can be brought in conjunction with Artificial Intelligence, Machine Learning, Information Retrieval, Text Mining, Knowledge Extraction and other related disciplines. This list comes from our day to day work on Language Models and NLP Tools. Read why this list is awesome. Our FAQ describes the important decisions and useful answers you may be interested in.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

pattern - Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization

  •    Python

It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD and available from http://www.clips.ua.ac.be/pages/pattern. This example trains a classifier on adjectives mined from Twitter using Python 3. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN or FAIL. The classifier uses the vectors to learn which other tweets look more like WIN or more like FAIL.

LSTM-Sentiment-Analysis - Sentiment Analysis with LSTMs in Tensorflow

  •    Jupyter

This repository contains the iPython notebook and training data to accompany the O'Reilly tutorial on sentiment analysis with LSTMs in Tensorflow. See the original tutorial to run this code in a pre-built environment on O'Reilly's servers with cell-by-cell guidance, or run these files on your own machine. There is also another file called Pre-Trained LSTM.ipynb which allows you to input your own text, and see the output of the trained network. Before running the notebook, you'll first need to download all data we'll be using. This data is located in the models.tar.gz and training_data.tar.gz tarballs. We will extract these into the same directory as Oriole LSTM.ipynb. As always, the first step is to clone the repository.

tf-rnn-attention - Tensorflow implementation of attention mechanism for text classification tasks.

  •    Python

Tensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).

Stocktalk - Data collection toolkit for social media analytics

  •    Python

Stocktalk is a visualization tool that tracks tweet volume and sentiment on Twitter, given a series of queries. It does this by opening a local websocket with Twitter and pulling tweets that contain user-specified keywords. For example, I can tell Stocktalk to grab all tweets that mention Ethereum and periodically tally volume and measure average sentiment every 15 minutes.

Obsei - Low code AI powered automation tool

  •    Python

Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more. It consist of Observer, Analyzer and Informer. Observer observes the platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information. Analyzer performs text analysis like classification, sentiment, translation, PII on the analyzed data. Informer sends it to ticketing system, data store, dataframe etc for further action and analysis.

awesome-sentiment-analysis - 😀😄😂😭 A curated list of Sentiment Analysis methods, implementations and misc


Curated list of Sentiment Analysis methods, implementations and misc. The goal of this repository is to provide adequate links for scholars who want to research in this domain; and at the same time, be sufficiently accessible for developers who want to integrate sentiment analysis into their applications.

spark-nlp - Natural Language Understanding Library for Apache Spark.

  •    Jupyter

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

twitter-sentiment-analysis - Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

  •    Jupyter

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods. The general requirements are as follows.

troll - Language sentiment analysis and neural networks... for trolls.

  •    Javascript

Troll is a tool for performing sentiment analysis (ie: "is this naughty or nice") on arbitrary blocks of text and associating it with a unique user. Using this data, combined with a rather naïve neural network and some training data, users can be indentified as "trolls".

twitter-sent-dnn - Deep Neural Network for Sentiment Analysis on Twitter

  •    Python

It returns a sentiment index ranging from 0 (negative sentiment) to 1 (positive sentiment). Please refer to A Convolutional Neural Network for Modelling Sentences for more information about the algorithm.

reactionrnn - Python module + R package to predict the reactions to a given text using a pretrained recurrent neural network

  •    Python

reactionrnn is a Python 2/3 module + R package on top of Keras/TensorFlow which can easily predict the proportionate reactions (love, wow, haha, sad, angry) to a given text using a pretrained recurrent neural network. Unlike traditional sentiment analysis models using tools like word2vec/doc2vec, reactionrnn handles text at the character level, allowing it to incorporate capitalization, grammar, text length, and sarcasm in its predictions.

dataflow-opinion-analysis - Opinion Analysis of News, Threaded Conversations, and User Generated Content

  •    Java

This sample uses Cloud Dataflow to build an opinion analysis processing pipeline for news, threaded conversations in forums like Hacker News, Reddit, or Twitter and other user generated content e.g. email.Opinion Analysis can be used for lead generation purposes, user research, or automated testimonial harvesting.

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