A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification

• Main Authors: Sakirin Tam, Rachid Ben Said, O. Ozgur Tanriover
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Natural Language Processing; sentiment analysis; CNN; Bi-LSTM; Word2Vec; GloVe
• Online Access: https://ieeexplore.ieee.org/document/9373371/

Being one of the most widely used social media tools, Twitter is seen as an important source of information for acquiring people’s attitudes, emotions, views and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Although CNN can extract high-level local features efficiently by using convolutional layer and max-pooling layer, it cannot effectively learn sequence of correlations. On the other hand, Bi-LSTM uses two LSTM directions to improve the contexts available to deep learning algorithms, but Bi-LSTM cannot extract local features in a parallel way. Therefore, applying a single CNN or single Bi-LSTM for sentiment analysis cannot achieve the optimal classification result. An integrating structure of CNN and Bi-LSTM model is proposed in this study. ConvBiLSTM is implemented; a word embedding model which converts tweets into numerical values, CNN layer receives feature embedding as input and produces smaller dimension of features, and the Bi-LSTM model takes the input from the CNN layer and produces classification result. Word2Vec and GloVe were distinctly applied to observe the impact of the word embedding result on the proposed model. ConvBiLSTM was applied with retrieved Tweets and SST-2 datasets. ConvBiLSTM model with Word2Vec on retrieved Tweets dataset outperformed the other models with 91.13% accuracy.