# Lexicon Integrated CNN Models with Attention for Sentiment Analysis

arXiv: https://arxiv.org/abs/1610.06272 · HTML: https://mlsanity.com/paper/1610.06272/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 8 of 8)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition](https://mlsanity.com/paper/2108.00194/) (2021)
- [A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods](https://mlsanity.com/paper/2106.08829/) (2021)
- [SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2105.12305/) (2021)
- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)
- [Lex2Sent: A bagging approach to unsupervised sentiment analysis](https://mlsanity.com/paper/2209.13023/) (2022)
- [Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis](https://mlsanity.com/paper/2403.01921/) (2024)
- [Using GAN-based models to sentimental analysis on imbalanced datasets in education domain](https://mlsanity.com/paper/2108.12061/) (2021)

## Abstract (arXiv)

With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention. Our models are experimented on both the SemEval'16 Task 4 dataset and the Stanford Sentiment Treebank, and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings allow to build high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.