Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the por of Twitter events to predict consumer sales events by analysing sales for 75 companies from the retail sector and over 150 million tets mentioning those companies along with their sentiment.  suggest an approach for events identification on Twitter extending existing methodologies of event study.  also propose a robust method for clustering Twitter events into different types based on their shape, which captures the varying dynamics of information propagation through the social network.  provide empirical evidence that through events differentiation based on their shape  can clearly identify types of Twitter events that have a more significant por to predict spikes in sales than the aggregated Twitter signal.
['Olga Kolchyna', 'Tharsis T. P. Souza', 'Tomaso Aste', 'Philip C. Treleaven']
I am writing a research paper on a idea In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales
[{"from": "human", "value": "I am writing a research paper on a idea In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the por of Twitter events to predict consumer sales events by analysing sales for 75 companies from the retail sector and over 150 million tets mentioning those companies along with their sentiment.  suggest an approach for events identification on Twitter extending existing methodologies of event study.  also propose a robust method for clustering Twitter events into different types based on their shape, which captures the varying dynamics of information propagation through the social network.  provide empirical evidence that through events differentiation based on their shape  can clearly identify types of Twitter events that have a more significant por to predict spikes in sales than the aggregated Twitter signal."}]