Name
Title | ||
|---|---|---|
Data Driven Sales Prediction Using Communication Sentiment Analysis in B2B CRM Systems |
Abstract | ||
|---|---|---|
In this work, we are proposing a methodology for data-driven decision making using sentiment analysis. The analysis of sentiment is done by text mining the activity notes recorded in Customer Relationship Management Systems used to manage complex sales in business to business environments. We built the sentiment enhanced sales prediction models using Artificial Neural Networks, Support Vector Machines and Random Forests and involving different sentiment features. The approach produced meaningful results with Random Forest obtaining the best improvement compared to a baseline model without sentiment features. The best model showed that new attributes incorporating sentiment information improved the accuracy from a baseline of 85.15% to 89.11 %. This model was used to conduct an analysis and an evaluation of the steps needed to be taken to win a possible losing deal in a real-world business to business customer relationship management system. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SYNASC49474.2019.00032 | 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) |
Keywords | DocType | ISSN |
Customer Relationship Management,Opinion Mining,Support Vector Machines,Random Forest,Sentiment Analysis | Conference | 2470-8801 |
ISBN | Citations | PageRank |
978-1-7281-5725-2 | 0 | 0.34 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doru Rotovei | 1 | 0 | 1.69 |
| Viorel Negru | 2 | 311 | 47.71 |