Sentiment analysis (SA) of natural language text is an important and challenging task for many applications of Natural Language Processing. Till now, researchers have used different types of SA techniques such as lexicon based and machine learning to perform SA for different languages such as English, Chinese. Inspired by the gain in popularity of deep learning models, we conducted experiments using different configuration settings of convolutional neural network (CNN) and performed SA of Hindi movie reviews collected from online newspapers and Web sites. The dataset has been manually annotated by three native speakers of Hindi to prepare it for training of the model. The experiments are conducted using different numbers of convolution layers with varying number and size of filters. The CNN models are trained on 50% of the dataset and tested on remaining 50% of the dataset. For the movie reviews dataset, the results given by our CNN model are compared with traditional ML algorithms and state-of-the-art results. It has been observed that our model is able to achieve better performance than traditional ML approaches and it has achieved an accuracy of 95%.

Deep Learning Based Sentiment Analysis Using Convolution Neural Network

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain...

Analyzing stock market trends using social media user moods and social influence

with the rapid development of network information technology, social media has become a new information dissemination platform, and has the characteristics of rapid dissemination and wide audience. This paper proposes a micro-blog commentary sentiment analysis method based on deep neural network. By constructing multi-level pooling layer and convolution layer, the network complexity is increased, and then the data features can be effectively extracted, the sentiment analysis accuracy rate can be improved, and the network can be avoided. Locally optimal. Firstly, the microblog commentary is preprocessed, then the word2vec algorithm is used for text representation, and the feature matrix is input into the convolutional neural network to extract the key features in the text. Finally, the softmax logistic regression network is used to classify the features and obtain the sentiment analysis results. Through experimental verification, the accuracy of this method is significantly improved compared with the traditional machine learning method.

Sentiment Analysis of Weibo Comments Based on Deep Neural Network

Emotion recognition systems are helpful in human–machine interactions and Intelligence Medical applications. Electroencephalogram (EEG) is closely related to the central nervous system activity of the brain. Compared with other signals, EEG is more closely associated with the emotional activity. It is essential to study emotion recognition based on EEG information. In the research of emotion recognition based on EEG, it is a common problem that the results of individual emotion classification vary greatly under the same scheme of emotion recognition, which affects the engineering application of emotion recognition. In order to improve the overall emotion recognition rate of the emotion classification system, we propose the CSP_VAR_CNN (CVC) emotion recognition system, which is based on the convolutional neural network (CNN) algorithm to classify emotions of EEG signals. Firstly, the emotion recognition system using common spatial patterns (CSP) to reduce the EEG data, then the standardized variance (VAR) is selected as the parameter to form the emotion feature vectors. Lastly, a 5-layer CNN model is built to classify the EEG signal. The classification results show that this emotion recognition system can better the overall emotion recognition rate: the variance has been reduced to 0.0067, which is a decrease of 64% compared to that of the CSP_VAR_SVM (CVS) system. On the other hand, the average accuracy reaches 69.84%, which is 0.79% higher than that of the CVS system. It shows that the overall emotion recognition rate of the proposed emotion recognition system is more stable, and its emotion recognition rate is higher.

Picture-induced EEG Signal Classification Based on CVC Emotion Recognition System

A new emotion analysis fusion and complementary model based on online food reviews

Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo’s sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

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An effective convolutional neural network model for Chinese sentiment analysis

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An effective convolutional neural network model for Chinese sentiment analysis