Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model
This work provides an incremental improvement for businesses seeking to better quantify customer value from social media data.
The paper tackled the problem of refining user contribution scores for businesses by analyzing Twitter sentiment, specifically focusing on identifying promotional tweets in the developer community, achieving a 90% accuracy rate in product-specific promotion detection.
In the last couple decades, social network services like Twitter have generated large volumes of data about users and their interests, providing meaningful business intelligence so organizations can better understand and engage their customers. All businesses want to know who is promoting their products, who is complaining about them, and how are these opinions bringing or diminishing value to a company. Companies want to be able to identify their high-value customers and quantify the value each user brings. Many businesses use social media metrics to calculate the user contribution score, which enables them to quantify the value that influential users bring on social media, so the businesses can offer them more differentiated services. However, the score calculation can be refined to provide a better illustration of a user's contribution. Using Microsoft Azure as a case study, we conducted Twitter sentiment analysis to develop a machine learning classification model that identifies tweet contents and sentiments most illustrative of positive-value user contribution. Using data mining and AI-powered cognitive tools, we analyzed factors of social influence and specifically, promotional language in the developer community. Our predictive model was a combination of a traditional supervised machine learning algorithm and a custom-developed natural language model for identifying promotional tweets, that identifies a product-specific promotion on Twitter with a 90% accuracy rate.