Document Modeling with Hierarchical Deep Learning Approach for Sentiment Classification

Ghosh, Monalisa; Sanyal, Goutam

doi:10.1145/3193025.3193046

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The authors performed the experiments on two movie review datasets. The experimental results showed that the accuracy of the proposed model is better than traditional machine learning methods such as Support Vector Machine, Multinomial Naïve Bayes as well as the standard Neural Network model on the sentiment classification dataset [11].…”

Section: Text Classification Using Machine Learningmentioning

confidence: 98%

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A Parallel Processing Technique for Filtering and Storing User Specified Data

Chanda¹

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Users are often interested in a specific type of data (user-preferred data) from a largevolume dataset. An efficient system that only stores user-preferred data from the large dataset can reduce the search latency, which allows the users to search for relevant information in a timely manner. The motivation behind this thesis is to devise a technique that filters a large dataset and stores only the filtered data, thereby saving storage space for the user. Running the filtering operation can be CPU-intensive, which can lead to high latency in extracting preferred data from the dataset. To solve this problem, the technique employs parallel processing and machine learning. A proof-of-concept prototype for this technique has been built on Apache Spark. The performance of the prototype subjected to synthetic datasets is analyzed. The analysis of experimental results shows the viability of this technique and provides insights into the system behavior and performance.

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Section: Text Classification Using Machine Learningmentioning

confidence: 98%

“…In the study presented in [10] [10]. The authors in [11] proposed a model to perform deep sentiment analysis for documents using a CNN and an LSTM network. The authors performed the experiments on two movie review datasets.…”

Section: Text Classification Using Machine Learningmentioning

confidence: 99%

A Parallel Processing Technique for Filtering and Storing User Specified Data

Chanda¹

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Knowledge-oriented Hierarchical Neural Network for Sentiment Classification

Wang

2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Sentiment classification aims to classify the sentimental polarities of given texts. Lexicon-based approaches utilize lexical resources to explore the opinions according to some specific rules, whose effectiveness strongly depends on the goodness of the lexical resources and the rules. Traditional machine-learning methods tightly rely on feature engineering and external NLP toolkits with unavoidable errors. Deep learning models strongly rely on a large amount of labelled data to train their numerous parameters, which often suffer from overfitting issue since it is difficult to obtain sufficient training data. To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment classification. Firstly K-CNN is used to capture the n-gram features in sentences. Sentiment word filters are constructed in the knowledge-oriented channel of K-CNN based on the linguistic knowledge from SentiWv ordNet, which can capture the sentiment lexicons and alleviate overfitting effectively. Then biGRU with attention mechanism is utilized to model the sequential relations between sentences and obtain the document-level representation based on the relevance of each sentence to the final sentiment classification. Experiments on two datasets show that our model outperforms other classical deep neural network models.

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Filtering and Storing User Preferred Data: an Apache Spark Based Approach

Chanda

Majumdar

2020

2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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Document Modeling with Hierarchical Deep Learning Approach for Sentiment Classification

Cited by 5 publications

References 16 publications

A Parallel Processing Technique for Filtering and Storing User Specified Data

A Parallel Processing Technique for Filtering and Storing User Specified Data

Knowledge-oriented Hierarchical Neural Network for Sentiment Classification

Filtering and Storing User Preferred Data: an Apache Spark Based Approach

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