Abstract
Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.
Highlights
With the proliferation of Web2.0, people are increasingly expressing and sharing their opinion through social media
We proposed optimization LSTM algorithm model for effective sentiment analysis
80% of the dataset are taken for training the proposed classifier Adaptive Particle Swarm Optimization (APSO)-LSTM and 20% of the dataset is taken for testing the classifier
Summary
With the proliferation of Web2.0, people are increasingly expressing and sharing their opinion through social media. Micro-blogging websites like Twitter are becoming a very popular communication tool The analysis of this site reveals a large number of social messages expressing their views and feelings on various topics and aspects of life. Because of this expansion, a lot of information is created. With the support of social media, people can They used for sharing their daily life events that lead to collecting large and different types of data. Skip-gram word embedding method is utilized to obtain the overall higher accuracy This word embedding model achieves superior results over other word representation.
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