One of the biggest issues of Indian economy in 2017 was the implementation of Goods and Services Tax (GST), and the social networks witnessed a lot of opinion contrasts and conflicts regarding this new taxation system. Inspired by such a large-scale tax reformation, we developed an experimental approach to analyze the reactions of public sentiment on Twitter based on popular words either directly or indirectly related to GST. We collected a number of almost 200 k tweets solely about GST from June 2017 to December 2017 in two phases. In order to assure the relevance of our crawled tweets with respect to GST, we prepared a topic-sentiment relevance model. Furthermore, we employed several state-of-the-art lexicons for identifying sentiment words and assigned polarity ratings to each of the tweets. On the other hand, in order to extract the relevant words that are linked with GST implicitly, we propose a new polarity-popularity framework and such popular words were also rated with sentiments. Next, we trained an LSTM model using both types of rated words for predicting sentiment on GST tweets and obtained an overall accuracy of 84.51%. It was observed that the performance of the system has been started improving while incorporating the knowledge of indirectly related GST words during training.

2017年，印度经济最大的问题之一是商品和服务税（GST）的实施，社交网络见证了关于这种新税收制度的许多意见对比和冲突。在如此大规模的税制改革的启发下，我们开发了一种实验方法，以基于与GST直接或间接相关的流行词来分析Twitter上公众情绪的反应。从2017年6月到2017年12月，我们分两个阶段收集了大约20万条有关GST的推文。为了确保我们抓取的推文与GST的相关性，我们准备了主题-情感相关性模型。此外，我们采用了几种最新的词典来识别情感词，并为每条推文分配极性等级。另一方面，为了隐式提取与GST相关的相关单词，我们提出了一个新的极性流行框架，并对此类流行单词也进行了评价。接下来，我们使用两种类型的词语来训练LSTM模型，以预测GST推文的情绪，并获得84.51％的总体准确性。据观察，系统的性能已经开始提高，同时在培训过程中结合了间接相关的GST词的知识。