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

TL;DR

This paper introduces an ensemble model combining CNN and Bi-LSTM for sentiment analysis, demonstrating improved accuracy over individual models and previous methods in analyzing social media and review data.

Contribution

The paper presents a novel ensemble approach of CNN and Bi-LSTM models that enhances sentiment analysis performance beyond existing neural network architectures.

Findings

Ensemble model outperforms individual CNN and Bi-LSTM models.

Achieves higher accuracy than previous sentiment analysis methods.

Effectively captures both local and temporal features of text.

Abstract

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.