sentimentAnalysis   596

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[1704.01444] Learning to Generate Reviews and Discovering Sentiment
We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.
sentimentanalysis 
3 days ago by hustwj
Joint sentiment/topic model for sentiment analysis
Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.
LDA  sentimentanalysis 
7 weeks ago by hustwj
Cambridge Analytica: the Geotargeting and Emotional Data Mining Scripts
“Cambridge Analytica: the Geotargeting and Emotional Data Mining Scripts” by How the public were manipulated
sentimentAnalysis  algorithmics  evilness  enrichment  dataEnrichment  from twitter_favs
8 weeks ago by psychemedia
Periscopic: Do good with data
Our resulting visualizations explore the emotional character of our new President using footage of his campaign speeches. Here, we’ll walk through our process of research, discovery, and refinement over the course of the project.
Brief Intro to the Emotion API
The tool at the core of our analysis was the Microsoft Emotion API. Given a video input, the API finds each face within the frame at an interval of roughly half a second and calculates confidence across a set of eight universally-recognized and communicated emotions: anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. The data produced can be used to monitor the predominant emotions of a single subject or even an entire crowd.
dj  vis  sentimentanalysis  trump  apis  api 
8 weeks ago by paulbradshaw
Get into Sentiment Analysis with Ruby - Red Panthers
Sometimes we fail to understand other’s emotion. So how it will be when machines try to understand ours? When writing programs we care about the syntax and structures but these concerns are not there in communication between people. To process our language machines have to understand not only what we say, but what we mean. Natural language processing is a fascinating subject to explore. But what makes it complicated?
ruby  nlp  sentimentanalysis 
11 weeks ago by moonhouse

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