# A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking

arXiv: https://arxiv.org/abs/1706.08032 · HTML: https://mlsanity.com/paper/1706.08032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [The Overlooked Repetitive Lengthening Form in Sentiment Analysis](https://mlsanity.com/paper/2604.01268/) (2026)
- [A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language](https://mlsanity.com/paper/2106.12797/) (2021)

## Abstract (arXiv)

This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.