LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments

Abstract

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. Data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%.