64,359 research outputs found
A Deep Learning Architecture for Sentiment Analysis
The fabulous results of Deep Convolution Neural Networks in computer vision and image analysis have recently attracted considerable attention from researchers of other application domains as well. In this paper we present NgramCNN, a neural network architecture we designed for sentiment analysis of long text documents. It uses pretrained word embeddings for dense feature representation and a very simple single-layer classifier. The complexity is encapsulated in feature extraction and selection parts that benefit from the effectiveness of convolution and pooling layers. For evaluation we utilized different kinds of emotional text datasets and achieved an accuracy of 91.2 % accuracy on the popular IMDB movie reviews. NgramCNN is more accurate than similar shallow convolution networks or deeper recurrent networks that were used as baselines. In the future, we intent to generalize the architecture for state of the art results in sentiment analysis of variable-length texts
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their application to the task of sentiment analysis is mostly unexplored and
there are few studies regarding how to design CNNs for this purpose. In this
work, we study the suitability of fine-tuning a CNN for visual sentiment
prediction as well as explore performance boosting techniques within this deep
learning setting. Finally, we provide a deep-dive analysis into a benchmark,
state-of-the-art network architecture to gain insight about how to design
patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and
Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
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.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
A sentiment information collector–extractor architecture based neural network for sentiment analysis
Sentiment analysis, also known as opinion mining is a key natural language processing (NLP) task that receives much attention these years, where deep learning based neural network models have achieved great success. However, the existing deep learning models cannot effectively make use of the sentiment information in the sentence for sentiment analysis. In this paper, we propose a Sentiment Information Collector–Extractor architecture based Neural Network (SICENN) for sentiment analysis consisting of a Sentiment Information Collector (SIC) and a Sentiment Information Extractor (SIE). The SIC based on the Bi-directional Long Short Term Memory structure aims at collecting the sentiment information in the sentence and generating the information matrix. The SIE takes the information matrix as input and extracts the sentiment information precisely via three different sub-extractors. A new ensemble strategy is applied to combine the results of different sub-extractors, making the SIE more universal and outperform any single sub-extractor. Experiments results show that the proposed architecture outperforms the state-of-the-art methods on three datasets of different language
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media
Sentiment analysis has been emerging recently as one of the major natural
language processing (NLP) tasks in many applications. Especially, as social
media channels (e.g. social networks or forums) have become significant sources
for brands to observe user opinions about their products, this task is thus
increasingly crucial. However, when applied with real data obtained from social
media, we notice that there is a high volume of short and informal messages
posted by users on those channels. This kind of data makes the existing works
suffer from many difficulties to handle, especially ones using deep learning
approaches. In this paper, we propose an approach to handle this problem. This
work is extended from our previous work, in which we proposed to combine the
typical deep learning technique of Convolutional Neural Networks with domain
knowledge. The combination is used for acquiring additional training data
augmentation and a more reasonable loss function. In this work, we further
improve our architecture by various substantial enhancements, including
negation-based data augmentation, transfer learning for word embeddings, the
combination of word-level embeddings and character-level embeddings, and using
multitask learning technique for attaching domain knowledge rules in the
learning process. Those enhancements, specifically aiming to handle short and
informal messages, help us to enjoy significant improvement in performance once
experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in
IJCVR on September 201
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