Displaying 1 to 20 from 20 results

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.

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".

node-germansentiment - german sentiment analysis

  •    Javascript

word-list based sentiment analysis for german language text

pytreebank - :rage::innocent: Stanford Sentiment Treebank loader in Python

  •    Python

Utilities for downloading, importing, and visualizing the Stanford Sentiment Treebank, a dataset capturing fine-grained sentiment over movie reviews. See examples below for usage. Tested in Python 3.4.3 and 2.7.12. Javascript code by Jason Chuang and Stanford NLP modified and taken from Stanford NLP Sentiment Analysis demo.

sentimentr - Dictionary based sentiment analysis that considers valence shifters

  •    R

sentimentr is designed to quickly calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s). sentimentr is a response to my own needs with sentiment detection that were not addressed by the current R tools. My own polarity function in the qdap package is slower on larger data sets. It is a dictionary lookup approach that tries to incorporate weighting for valence shifters (negation and amplifiers/deamplifiers). Matthew Jockers created the syuzhet package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods as well as a custom dictionary. He also utilizes a wrapper for the Stanford coreNLP which uses much more sophisticated analysis. Jocker's dictionary methods are fast but are more prone to error in the case of valence shifters. Jocker's addressed these critiques explaining that the method is good with regard to analyzing general sentiment in a piece of literature. He points to the accuracy of the Stanford detection as well. In my own work I need better accuracy than a simple dictionary lookup; something that considers valence shifters yet optimizes speed which the Stanford's parser does not. This leads to a trade off of speed vs. accuracy. Simply, sentimentr attempts to balance accuracy and speed.


  •    R

stansent wraps Stanford's coreNLP sentiment tagger in a way that makes the process easier to get set up. The output is designed to look and behave like the objects from the sentimentr package. Plotting and the sentimentr::highlight functionality will work similar to the sentiment/sentiment_by objects from sentimentr. This requires less learning to work between the two packages. In addition to sentimentr and stansent, Matthew Jocker's has created the syuzhet package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods. Similarly, Subhasree Bose has contributed RSentiment which utilizes dictionary lookup that atempts to address negation and sarcasm. Click here for a comparison between stansent, sentimentr, syuzhet, and RSentiment. Note the accuracy and run times of the packages.

salient - Machine Learning, Natural Language Processing and Sentiment Analysis Toolkit for Node.js

  •    Javascript

Part of speech tagging is done primarily through the use of the trigram hidden-markov model. While there are many methods used since then, Trigram HMM, seems to be the easiest to implement while maintaining an effective accuracy. This was built through the use of several resources online including bootstrapping the vocabulary using Wiktionary (https://www.wiktionary.org/). This is a common alternative technique to the unsupervised learning technique by providing a bit of an edge to the model with an existing dictionary of sorts. In some cases, the dictionary can be generated from a part of speech corpus (sometimes manually or automatically tagged). On top of Wiktionary, I am using several corpus to build the English language model including: Brown Corpus, Penn TreeBank, Twitter TreeBank. These treebanks provide a resource for calculating and training the model for supervised learning cases. The actually tagging portion is done using the Viterbi path finding algorithm implemented for all standard models. The spanish model is trained using the IULA Spanish LSP TreeBank. You will notice both models are stored in the bin directory.

Emotion_and_Polarity_SO - An emotion classifier of text containing technical content from the SE domain

  •    OpenEdge

F. Calefato, F. Lanubile, N. Novielli. “EmoTxt: A Toolkit for Emotion Recognition from Text” To appear in In Proceedings of the Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, {ACII} Workshops 2017, San Antonio, USA, Oct. 23-26, 2017, pp. 79-80, ISBN: 978-1-5386-0563-9. In the following, we show first how to train a new model for emotion classification and, then, how to test the model on unseen data.

Senti4SD - An emotion-polarity classifier specifically trained on developers' communication channels

  •    R

Senti4SD is an emotion polarity classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and evaluated on a gold standard of over 4K posts extracted from Stack Overflow. where inputCorpus.csv is a file containing the data you want to classify, considering a document for each line, and outputPredictions.csv is where the predictions will be saved. This last parameter is optional, if not present the output of the classification will be saved in a file called predictions.csv.

watson-discovery-food-reviews - Combine Watson Knowledge Studio and Watson Discovery to discover customer sentiment from product reviews

  •    Javascript

In this Code Pattern, we walk you through a working example of a web application that queries and manipulates data from the Watson Discovery Service. With the aid of a custom model built with Watson Knowledge studio, the data will have additional enrichments that will provide improved insights for user analysis. This web app contains multiple UI components that you can use as a starting point for developing your own Watson Discovery and Knowledge Studio service applications.

sentiment-2017-imavis - From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction

  •    Python

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model. Our article can be found on ScienceDirect. A preprint is publicly available on arXiv as well. You can also find it indexed on gitxiv.

sentiment-analysis - :balloon: A Node.js AFINN-111 based sentiment analysis module

  •    CoffeeScript

sentiment-analysis will return a score between -1 and +1, where negative numbers represent a negative overall sentiment. For further examples of usage, see this Example Gist.

node-datumbox - DatumBox API wrapper in Node.js

  •    Javascript

This module is designed to make the DatumBox API available in Node.js. Its pretty inspired and ported from PHP Wrapper. The Datumbox is a web service which allows you to use our tools from your website, software or mobile application. The API gives you access to all of the supported functions of our service. In this page you will find all the information that you need in order to use our API, fully implemented code samples and the latest API Documentation.

emoji-sentiment - Emoji sentiment data

  •    Javascript

Have a look at this table to see an example of what data this library provides. Array of emoji sentiment data.

media-bias - Measures public sentiment on Twitter towards politicians

  •    Python

Measures public sentiment on Twitter towards politicians. Deployed as Political Discussion.

retext-sentiment - plugin to detect sentiment

  •    Javascript

Stability: Legacy. This package is no longer recommended for use. It’s still covered by semantic-versioning guarantees and not yet deprecated, but use of this package should be avoided. Please use a different way to detect sentiment, such as with words/polarity. Legacy documentation for this package is still available in Git.

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