Abstract
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.
Highlights
Internet data grow rapidly, given the preference of citizens to share their views.Through the expansion of social media, people’s opinion tools have been updated, and fields such as opinion mining and sentiment analysis have obtained growing demands.Online reviews cannot be overlooked owing to the possible effects that customer feedback may have on companies
We evaluated the performance of the proposed model in three classes using testing data and compared the results with other approaches on the same Kaggle datasets [7] to validate the advantage of our suggested technique
Based on the theoretical study of the ULMFit-Support Vector Machine (SVM), the parameters and hidden unit number setting in our model demonstrate that the hidden unit numbers and parameters are the most important parameters determining classification accuracy and training time speed
Summary
Through the expansion of social media, people’s opinion tools have been updated, and fields such as opinion mining and sentiment analysis have obtained growing demands. Online reviews cannot be overlooked owing to the possible effects that customer feedback may have on companies. A significant number of research practitioners are currently developing structures that can collect information from such feedback to support marketing insight, drive public sentiment, and enhance consumer loyalty. Opinion analysis was implemented and applied in several study areas and companies. A message on Twitter (similar to a post on Facebook), by a sequence of characters confined to 280-character limit, is posted publicly or by established followers owing to the account’s privacy, which is different from other social media websites [1]
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