On One Approach of Solving Sentiment Analysis Task for Kazakh and Russian Languages Using Deep Learning

Sentiment Analysis of Film Reviews Based on BI-GRU +Attention+Capsule Fusion

10.36227/techrxiv.14863401.v1 ◽

2021 ◽

Author(s):

zhifei hu

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Film Review ◽

The Other ◽

Learning Models ◽

Analysis Model ◽

Fusion Model ◽

Data Set ◽

Analysis Task ◽

Film Reviews

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.<br>

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Comparison of Deep Learning and Rule-based Method for the Sentiment Analysis Task

2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) ◽

10.1109/fareastcon50210.2020.9271333 ◽

2020 ◽

Author(s):

A.V. Kotelnikova

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Rule Based ◽

Analysis Task

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A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

Electronics ◽

10.3390/electronics10101133 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1133

Author(s):

Zenun Kastrati ◽

Lule Ahmedi ◽

Arianit Kurti ◽

Fatbardh Kadriu ◽

Doruntina Murtezaj ◽

...

Keyword(s):

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Large Scale ◽

Research Study ◽

Low Resource ◽

Analysis Task ◽

Large Scale Dataset ◽

Social Media Platforms ◽

Experimental Findings

During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.

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Sentiment Analysis of Film Reviews Based on BI-GRU +Attention+Capsule Fusion

10.36227/techrxiv.14863401 ◽

2021 ◽

Author(s):

zhifei hu

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Film Review ◽

The Other ◽

Learning Models ◽

Analysis Model ◽

Fusion Model ◽

Data Set ◽

Analysis Task ◽

Film Reviews

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.<br>

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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