Abstract
Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.
Highlights
Nowadays, people express their feelings and opinions to exchange their views using social media, such as Twitter, Facebook, Weibo, LinkedIn, and WeChat
We found that our proposed model has a significant accuracy of 94.01% for this dataset, with Recurrent Neural Network (RNN)-term frequency-inverse document frequency (TF-IDF) [34] remaining the lowest compared to all other models
This work presents a novel ACL-sentiment analysis (SA) model to tackle the lack of semantic information, high dimensionality, and overfitting problems
Summary
People express their feelings and opinions to exchange their views using social media, such as Twitter, Facebook, Weibo, LinkedIn, and WeChat. Bi-LSTM is employed to extract the contextual information from the feature-generated CNN layers to perform the sentiment analysis. Since sentiment analysis is one of the valuable decision-making methods, most of the work has been done in sentiment classification using data mining, machine learning algorithms, and a knowledge based approach [12]. Lexicon sentiment is performed, and a product identification model builds to detect the comparative social media content They presented the essential advantages of the target product compared to its competitors. These approaches are usually less accurate [17,18] Another major challenge of sentiment analysis on machine learning and lexicon-based, including the hybrid method, is feature selection, typically domain-dependent. DL is known for the multiple representation learning levels in machine learning and has recently been applied to sentiment analysis with significant results [20]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.