# Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models

arXiv: https://arxiv.org/abs/1904.04206 · HTML: https://mlsanity.com/paper/1904.04206/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning](https://mlsanity.com/paper/2107.02759/) (2021)
- [Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity](https://mlsanity.com/paper/2410.16410/) (2024)
- [JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models](https://mlsanity.com/paper/2505.13244/) (2025)

## Abstract (arXiv)

With the popularity of social networks, and e-commerce websites, sentiment analysis has become a more active area of research in the past few years. On a high level, sentiment analysis tries to understand the public opinion about a specific product or topic, or trends from reviews or tweets. Sentiment analysis plays an important role in better understanding customer/user opinion, and also extracting social/political trends. There has been a lot of previous works for sentiment analysis, some based on hand-engineering relevant textual features, and others based on different neural network architectures. In this work, we present a model based on an ensemble of long-short-term-memory (LSTM), and convolutional neural network (CNN), one to capture the temporal information of the data, and the other one to extract the local structure thereof. Through experimental results, we show that using this ensemble model we can outperform both individual models. We are also able to achieve a very high accuracy rate compared to the previous works.