Topic-level sentiment analysis of social media data using deep learning

Abstract Due to the inception of Web 2.0 and freedom to facilitate the dissemination of information, sharing views, expressing opinions with regards to current world level events, services, products, etc. social media platforms have been mainly contributing to user-generated content. Such social media data consist of various themes discussed online and are associated with sentiments of the users. To catch up with the speed of streaming data at which it generates on social media platforms, it is crucial to detect the topics being discussed on social media platforms and analyze the sentiments of users towards those topics in an online manner to make timely decisions. Motivated by the same, this paper proposes a deep learning based topic-level sentiment analysis model. The novelty of the proposed approach is that it works at the sentence level to extract the topic using online latent semantic indexing with regularization constraint and then applies topic-level attention mechanism in long short-term memory network to perform sentiment analysis. The proposed model is unique in the sense that it supports scalable and dynamic topic modeling over streaming short text data and performs sentiment analysis at topic-level. For SemEval-2017 Task 4 Subtask B dataset as a case of in-domain topic-level sentiment analysis, average recall of 0.879 has been achieved, whereas, for out-of-domain data, average recall of 0.846, 0.824 and 0.794 has been achieved for newly developed datasets collected under the hashtags #ethereum, #bitcoin and #facebook from Twitter. To assess the performance of the model for scalability, we analyzed the model in terms of average time in milliseconds for creation of feature vectors, throughput in terms of topics detected per second and average response time in seconds to handle the sentiment analysis queries. The experimental results are significant enough to enable large scale topic modeling over streaming data and perform topic-level sentiment analysis.