Abstract
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.
Highlights
Sentiment analysis refers to the use of text analysis and computational linguistic technique in NLP to identify, extract, and classify subjective information from unstructured text [1]
EXPERIMENTAL RESULTS Based on the study with best hyper-parameter tuning, the proposed model was experimented with tweets dataset and SST2 dataset against other deep learning models, including Convolution Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and CNN-LSTM
To verify the model with state-of-the-art, the experimental results were benchmarked with previous studies in text sentiment classification approaches on SST-2 dataset
Summary
Sentiment analysis ( known as opinion mining) refers to the use of text analysis and computational linguistic technique in NLP to identify, extract, and classify subjective information from unstructured text [1]. It aims to identify the polarity of sentences based on word clues extracted from the context of sentences [2]–[4]. DEEP LEARNING Recently, deep learning algorithms have achieved remarkable results in natural language processing area They represent data in multiple and successive layers. This is the reason why deep learning models have attracted attention from NLP researchers to explore sentiment classification
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
