Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

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Hybrid Deep Learning Models for Sentiment Analysis

Sentiment analysis technology generates linguistic understanding from the perspective of machines through the processing and analysis of large quantities of data, which is a hot research direction in the field of artificial intelligence. In this work, deep learning is used to build a sentence sentiment analysis system based on word vector space model and Recurrent Neural Network. Specifically, the GloVe word model is adopted here with the aim of making the best use of the global and local information for the training corpus,. In order to overcome the defect that traditional recurrent neural network cannot learn the long-term information of text, a neural network model combining LSTM and GRU is proposed in this work. Through the contrast experiment of RNN, LSTM and GRU, comparing the training time and test accuracy indicators, it is found that LSTM-GRU model performs best.

Sentiment Analysis based on GloVe and LSTM-GRU

A heterogeneous stacking ensemble based sentiment analysis framework using multiple word embeddings

A survey on deep learning based sentiment analysis

Comparative Analysis for Sentiment in Tweets Using LSTM and RNN

Nowadays, a million users use social networking services such as Twitter to tweet their products and services by placing the reviews based on their opinions. Sentiment analysis has emerged to analyze the twitter data automatically. Sentiment classification techniques used to classify US airline tweets based on sentiment polarity due to flight services as positive, negative and neutral connotations done on six different US airlines. To detect sentiment polarity, we explored word embedding models (Word2Vec, Glove) in tweets using deep learning methods. Here, we investigated sentiment analysis using the Recurrent Neural Network (RNN) model along with Long-Short Term Memory networks (LSTMs) units can deal with long term dependencies by introducing memory in a network model for prediction and visualization. The results showed better significant classification accuracy trained 80% for training set and 20% for testing set which shows that our models are reliable for future prediction. To improve this performance, the Bidirectional LSTM Model (Bi-LSTM) is used for further investigation studies.

R. Monika

Papers

Sentiment Analysis of US Airlines Tweets Using LSTM/RNN