Context-aware sliding window for sentiment classification

Abstract

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods.