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GCP Sentiment Analysis Tutorial
"This tutorial is designed to let you quickly start exploring and developing applications with the Google Cloud Natural Language API. It is designed for people familiar with basic programming, though even without much programming knowledge, you should be able to follow along. Having walked through this tutorial, you should be able to use the Reference documentation to create your own basic applications.

This tutorial steps through a Natural Language API application using Python code. The purpose here is not to explain the Python client libraries, but to explain how to make calls to the Natural Language API. Applications in Java and Node.js are essentially similar. Consult the Natural Language API Samples for samples in other languages (including this sample within the tutorial)."
machinelearning  nlp  semantics  sentiment  linguistics 
6 days ago by trewbot
MAchine Learning for LanguagE Toolkit
"MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text."
linguistics  lib  software  java  semantics  sentiment  nlp  machinelearning 
6 days ago by trewbot
Sentiment Analysis of 5 popular romantic comedies
Short tutorial on analyzing a sentiment in romantic comedies and creating interesting data visualizations to present how mood changes over time in the movies plot.
r  sentiment  text  datascience 
8 days ago by grandeabobora
A Framework for Supply Chain Leaders to Understand Supply Chain Analytics - by @lcecere
"While blockchain and cognitive computing are believed to be the most disruptive within five years, less than 7% of companies are actively testing these technologies today. While immature, the two together offer great promise. We continue to push the envelope to help companies test these technologies through the share group for the network of networks. The next session is at the GS1 Headquarters in Chicago on April 4th-5th. Let me know if you are interested in joining the discussion and learning more."
big  data  supply  chains  global  chain  analytics  insights  community  cognitive  descriptive  ontology  reporting  schema  on  read  sentiment  analysis 
17 days ago by jonerp
How to solve most NLP problems: a step-by-step guide | Hacker News
Word2Vec and bag-of-words/tf-idf are somewhat obsolete in 2018 for modeling. For classification tasks, fasttext ( performs better and faster.
Fasttext is also available in the popular NLP Python library gensim, with a good demo notebook:

And of course, if you have a GPU, recurrent neural networks (or other deep learning architectures) are the endgame for the remaining 10% of problems (a good example is SpaCy's DL implementation: Or use those libraries to incorporate fasttext for text encoding, which has worked well in my use cases.
nlp  word2vec  hn  ai  ml  classification  text-classification  text-analysis  sentiment 
29 days ago by hellsten
RT brisanborn: Recently, NDR Crowd Poll hit an all-time high (extreme optimism). Historically, peak opti…
Sentiment  from twitter
29 days ago by rhyndes
Introduction to LSTMs with TensorFlow - O'Reilly Media
In this tutorial, we will introduce the LSTM network architecture and build our own LSTM network to classify stock market sentiment from messages on StockTwits. We use TensorFlow because it offers compact, high-level commands and is very popular these days.
deeplearning  neuralnetworks  TensorFlow  LSTM  RNN  sentiment  stockmarket  StockTwits 
5 weeks ago by areich
Developer Happiness: Gratitude as an attitude
It’s a new year and many of us will soon set then forget our new year’s resolutions. One of the challenges I have personally is to maintain a grateful and co...
programming  sentiment  gratitude  advocacy 
7 weeks ago by gilberto5757
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
"Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used as baseline methods for text classification, but their performance varies greatly depending on the model variant, features used and task/dataset. We show that: (i) the inclusion of word bigram features gives consistent gains on sentiment analysis tasks; (ii) for short snippet sentiment tasks, NB actually does better than SVMs (while for longer documents the opposite result holds); (iii) a simple but novel SVM variant using NB log-count ratios as feature values consistently performs well across tasks and datasets. Based on these observations, we identify simple NB and SVM variants which outperform most published results on sentiment analysis datasets, sometimes providing a new state-of-the-art performance level."
papers  machine-learning  nlp  sentiment  christopher-manning 
7 weeks ago by arsyed
How Machine Learning Predicts Who Wrote The Tweets – Did Trump Tweet It?
e VADER Sentiment Analysis of Social Media Text algorithm. This is an off-the-shelf (but still state-of-the art) natural language processing code to estimate the sentiment present in the tweet.
textanalysis  sentiment  emotion 
7 weeks ago by spaetz

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