Web 2.0 technology enables customers to share electronic word of mouth (eWOM) about their experiences. eWOM offers great market insights to the organization, and important for organization’s success. eWOM monitoring and management is one of the major contemporary challenges for the organization, because of high volume and frequency of the content. It is nearly impossible for an organization to manually monitor content generated by each user. In this paper, we propose sentiment analysis as an alternative method for analysis of emotions and behavioral intentions in real-time data. Sentiment analysis is performed on women’s e-clothing reviews collected from the Kaggle data repository. The dataset consists of 23,486 reviews, comprising ten feature variables. This study applied artificial neural network techniques to determine polarity of the data in terms of positive or negative. Sentiment analysis was performed by using two artificial neural networks, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to classify the review as recommended (positive) or not recommended (negative). The proposed models have been evaluated on these performance measuring parameters: accuracy, recall, specificity, F1-score and roc-curve. The LSTM method outperformed CNN and achieved classification accuracy of 91.69%, specificity 92.81%, sensitivity 76.95%, and 56.67% F1-score. Based on results of this study, LSTM technique is highly recommended for the sentiment analysis of unstructured text-based user-generated content.
Sentiment Analysis of Microblogs Using Multilayer Feed-Forward Artificial Neural Networks