User’s Review Habits Enhanced Hierarchical Neural Network for Document-Level Sentiment Classification

Document-level sentiment classification is to assign an overall sentiment polarity to an opinion document. Some researchers have already realized that, in addition to document texts, extensional-information such as product features and user preferences can be quite useful. Many previous studies represent them as ID-type extensional-information and incorporate them into deep learning models. However, they ignore the descriptive extensional information that is also useful for document representations. This paper covers the following aspects: (1) introduces the Description of Opinion Target (DOT), a new extensional-information for document-level sentiment classification, (2) builds the Document-level Sentiment ClassificatioN with EXTensional-information (DSC_NEXT) dataset which consists of three datasets: IMDB_NEXT, Yelp_NEXT and CMRDB_NEXT and (3) validates the effectiveness of DOT by performing experiments based on current state-of-the-art (SOTA) document-level sentiment analysis methods. Implications for using extensional-information in neural network models are also considered.

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TransCapsule Model for Sentiment Classification

Journal of Artificial Intelligence and Capsule Networks - September 2019 ◽

10.36548/jaicn.2020.3.003 ◽

2020 ◽

Vol 2 (3) ◽

pp. 163-169

Author(s):

Dr. Akey Sungheetha ◽

Dr. Rajesh Sharma R,

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Network Model ◽

Online Reviews ◽

Point Of View ◽

Sentiment Classification ◽

The Other ◽

Learning Framework ◽

Very High ◽

Document Level

Aspect-level sentiment classification is the aspect of determining the text in a given document and classifying it according to the sentiment polarity with respect to the objective. However, since annotation cost is very high, it might serve a big obstacle for this purpose. However, from a consumer point of view, this is highly effective in reading document-level labelled data such as reviews which are present online using neural network. The online reviews are packed with sentiment encoded text which can be analyzed using this proposed methodology. In this paper a Transfer Capsule Network model is used which has the ability to transfer the knowledge gained at document-level to the aspect-level to classify according to the sentiment detected in the text. As the first step, the sentence is broken down in semantic representations using aspect routing to form semantic capsule data of both document-level and aspect-level. This routing approach is extended to group the semantic capsules for transfer learning framework. The effectiveness of the proposed methodology are experimented and demonstrated to determine how superior they are to the other methodologies proposed.

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Evolutionary Structure Optimization of Hierarchical Neural Network for Image Recognition

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.983 ◽

2011 ◽

Vol 131 (5) ◽

pp. 983-989

Author(s):

Satoru Suzuki ◽

Yasue Mitsukura

Keyword(s):

Neural Network ◽

Image Recognition ◽

Structure Optimization ◽

Hierarchical Neural Network

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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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