# A Novel Deep Learning Method for Textual Sentiment Analysis

arXiv: https://arxiv.org/abs/2102.11651 · HTML: https://mlsanity.com/paper/2102.11651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://mlsanity.com/paper/1512.05193/) (2015)
- [A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1801.06480/) (2018)
- [Structural Attention Neural Networks for improved sentiment analysis](https://mlsanity.com/paper/1701.01811/) (2017)

## Built on by (most cited first, 1 of 1)

- [Combination of Convolutional Neural Network and Gated Recurrent Unit for Energy Aware Resource Allocation](https://mlsanity.com/paper/2106.12178/) (2021)

## Abstract (arXiv)

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance.